14.2.18 Support Vector Machines, SVM

Chapter Contents (Back)
Support Vector Machines. SVM. Heavily referenced in the face recognition literature. Those are in the face recognition sections. Subsections are somewhat arbitrary. See also Training Support Vector Machines, SVM Training, Learning. Specific applications in other sections. See also Support Vector Machines, SVM, Applied to Recognition. See also Support Vector Machines, SVM, Incremental, Multi-Step.

Chen, S., Gunn, S.R., Harris, C.J.,
Decision feedback equaliser design using support vector machines,
VISP(147), No. 3, 2000, pp. 213-219. 0008
BibRef

Dhanjal, C.[Charanpal], Gunn, S.R.[Steve R.], Shawe-Taylor, J.[John],
Efficient Sparse Kernel Feature Extraction Based on Partial Least Squares,
PAMI(31), No. 8, August 2009, pp. 1347-1361.
IEEE DOI 0906
Dealing with irrelevant features in classificaton. BibRef

Drezet, P.M.L.[Pierre M.L.], Harrison, R.F.[Robert F.],
A new method for sparsity control in support vector classification and regression,
PR(34), No. 1, January 2001, pp. 111-125.
WWW Link. 0010
BibRef

Mangasarian, O.L.[Olvi L.], Musicant, D.R.[David R.],
Robust Linear and Support Vector Regression,
PAMI(22), No. 9, September 2000, pp. 950-955.
IEEE DOI 0010
BibRef

Mangasarian, O.L.[Olvi L.], Wild, E.W.[Edward W.],
Multisurface Proximal Support Vector Machine Classification via Generalized Eigenvalues,
PAMI(28), No. 1, January 2006, pp. 69-74.
IEEE DOI 0512
BibRef

Pedroso, J.P.[João Pedro], Murata, N.[Noboru],
Support Vector Machines with Different Norms: Motivation, Formulations and Results,
PRL(22), No. 12, October 2001, pp. 1263-1272.
Elsevier DOI 0108
BibRef

Guillamet, D.[David], Vitrià, J.[Jordi],
Discriminant Local Regions Using Support Vector Machines,
ELCVIA(1), 2002, pp. None. Reference to this is wrong. See conference paper. 0206

Earlier:
Local Discriminant Regions Using Support Vector Machines for Object Recognition,
SSSPR00(559-559).
WWW Link. BibRef

Zhou, W.D.[Wei-Da], Zhang, L.[Li], Jiao, L.C.[Li-Cheng],
Linear programming support vector machines,
PR(35), No. 12, December 2002, pp. 2927-2936.
WWW Link. 0209
BibRef

Zhang, L.[Li], Zhou, W.D.[Wei-Da], Jiao, L.C.[Li-Cheng],
Wavelet Support Vector Machine,
SMC-B(34), No. 1, February 2004, pp. 34-39.
IEEE Abstract. 0403
BibRef

Zhang, L.[Li], Zhou, W.D.[Wei-Da],
Density-induced margin support vector machines,
PR(44), No. 7, July 2011, pp. 1448-1460.
Elsevier DOI 1103
Support vector machine; Maximum margin classifier; Machine learning; Relative density degree BibRef

Chua, K.S.[Kok Seng],
Efficient computations for large least square support vector machine classifiers,
PRL(24), No. 1-3, January 2003, pp. 75-80.
Elsevier DOI 0211
BibRef

Davy, M., Gretton, A., Doucet, A., Rayner, P.J.W.,
Optimized support vector machines for nonstationary signal classification,
SPLetters(9), No. 12, December 2002, pp. 442-445.
IEEE Top Reference. 0301
BibRef

Parrado-Hernández, E.[Emilio], Mora-Jiménez, I., Arenas-García, J., Figueiras-Vidal, A.R., Navia-Vázquez, A.,
Growing support vector classifiers with controlled complexity,
PR(36), No. 7, July 2003, pp. 1479-1488.
WWW Link. 0304
BibRef

García-García, D.[Darío], Parrado Hernández, E.[Emilio], Díaz-de María, F.[Fernando],
A New Distance Measure for Model-Based Sequence Clustering,
PAMI(31), No. 7, July 2009, pp. 1325-1331.
IEEE DOI 0905
based on the Kullback-Leibler divergence. BibRef

Garcia-Garcia, D.[Dario], Parrado-Hernandez, E.[Emilio], Diaz-de-Maria, F.[Fernando],
State-space dynamics distance for clustering sequential data,
PR(44), No. 5, May 2011, pp. 1014-1022.
Elsevier DOI 1101
Sequential data; Clustering; Hidden Markov models BibRef

Muñoz-Romero, S.[Sergio], Gómez-Verdejo, V.[Vanessa], Parrado-Hernández, E.[Emilio],
A novel framework for parsimonious multivariate analysis,
PR(71), No. 1, 2017, pp. 173-186.
Elsevier DOI 1707
Feature, selection BibRef

Fei, Y.N., Lu, Z., Tang, W.H., Wu, Q.H.,
Harmonic Estimation Using a Global Search Optimiser,
EvoIASP07(261-270).
Springer DOI 0704
BibRef

Lau, K.W., Wu, Q.H.,
Local prediction of non-linear time series using support vector regression,
PR(41), No. 5, May 2008, pp. 1556-1564.
WWW Link. 0711
Time series analysis; Local prediction; Support vector regression; Radial basis function; Least square; Delay coordinates; State space reconstruction BibRef

Chen, Y.S.[Yi-Song], Wang, G.P.[Guo-Ping], Dong, S.H.[Shi-Hai],
Learning with progressive transductive support vector machine,
PRL(24), No. 12, August 2003, pp. 1845-1855.
WWW Link. 0304
BibRef

Steinwart, I.[Ingo],
On the optimal parameter choice for v-support vector machines,
PAMI(25), No. 10, October 2003, pp. 1274-1284.
IEEE Abstract. 0310
See also New Support Vector Algorithms. The parameter v should be twice the optimal Bayes risk. BibRef

Rojo Alvarez, J.L., Martinez Ramon, M., Figueiras Vidal, A.R., Garcia Armada, A., Artes Rodriguez, A.,
A robust support vector algorithm for nonparametric spectral analysis,
SPLetters(10), No. 11, November 2003, pp. 320-323.
IEEE Abstract. 0310
BibRef

Maruyama, K.I.[Ken-Ichi], Maruyama, M.[Minoru], Miyao, H.[Hidetoshi], Nakano, Y.[Yasuaki],
A method to make multiple hypotheses with high cumulative recognition rate using SVMs,
PR(37), No. 2, February 2004, pp. 241-251.
WWW Link. 0311
BibRef

Karaçali, B.[Bilge], Ramanath, R.[Rajeev], Snyder, W.E.[Wesley E.],
A comparative analysis of structural risk minimization by support vector machines and nearest neighbor rule,
PRL(25), No. 1, January 2004, pp. 63-71.
WWW Link. 0311
BibRef

Mitra, P.[Pabitra], Murthy, C.A., Pal, S.K.[Sankar K.],
A Probabilistic Active Support Vector Learning Algorithm,
PAMI(26), No. 3, March 2004, pp. 413-418.
IEEE Abstract. 0402
Rather than points based on proximity to the separating hyperplane, use points according to a distribution determined by the hyperplane and confidence factor. BibRef

Chen, J.H.[Jiun-Hung], Chen, C.S.[Chu-Song],
Reducing SVM Classification Time Using Multiple Mirror Classifiers,
SMC-B(34), No. 2, April 2004, pp. 1173-1183.
IEEE Abstract. 0404
BibRef
Earlier:
Speeding up SVM decision based on mirror points,
ICPR02(II: 869-872).
IEEE DOI 0211
BibRef

Lee, J.W.[Jae-Wook], Lee, D.W.[Dae-Won],
An Improved Cluster Labeling Method for Support Vector Clustering,
PAMI(27), No. 3, March 2005, pp. 461-464.
IEEE Abstract. 0501
BibRef

Lee, J.W.[Jae-Wook], Lee, D.W.[Dae-Won],
Dynamic Characterization of Cluster Structures for Robust and Inductive Support Vector Clustering,
PAMI(28), No. 11, November 2006, pp. 1869-1874.
IEEE DOI 0609
BibRef

Lee, D.W.[Dae-Won], Lee, J.W.[Jae-Wook],
Domain described support vector classifier for multi-classification problems,
PR(40), No. 1, January 2007, pp. 41-51.
WWW Link. 0611
Multi-class classification; Kernel methods; Bayes decision theory; Density estimation; Support vector domain description BibRef

Jung, K.H.[Kyu-Hwan], Lee, D.W.[Dae-Won], Lee, J.W.[Jae-Wook],
Fast support-based clustering method for large-scale problems,
PR(43), No. 5, May 2010, pp. 1975-1983.
Elsevier DOI 1003
Large-scale problem; Kernel methods; Support vector clustering; Cluster labeling; Dynamical system BibRef

Lee, K.Y.[Ki-Young], Kim, D.W.[Dae-Won], Lee, K.H.[Kwang H.], Lee, D.[Doheon],
Possibilistic support vector machines,
PR(38), No. 8, August 2005, pp. 1325-1327.
WWW Link. 0505
BibRef

Ayat, N.E., Cheriet, M., Suen, C.Y.,
Automatic model selection for the optimization of SVM kernels,
PR(38), No. 10, October 2005, pp. 1733-1745.
WWW Link. 0508
BibRef

Adankon, M.M.[Mathias M.], Cheriet, M.[Mohamed],
Optimizing resources in model selection for support vector machine,
PR(40), No. 3, March 2007, pp. 953-963.
WWW Link. 0611
Model selection; SVM; Kernel; Hyperparameters; Optimizing time See also Help-Training for semi-supervised support vector machines. BibRef

Zhang, J.Y.[Jia-Yong], Liu, Y.X.[Yan-Xi],
SVM decision boundary based discriminative subspace induction,
PR(38), No. 10, October 2005, pp. 1746-1758.
WWW Link. 0508
BibRef

González, L., Angulo, C., Velasco, F., Català, A.,
Unified dual for bi-class SVM approaches,
PR(38), No. 10, October 2005, pp. 1772-1774.
WWW Link. 0508
BibRef
Earlier: More developed version:
Dual unification of bi-class support vector machine formulations,
PR(39), No. 7, July 2006, pp. 1325-1332.
WWW Link. 0606
Large margin principle; Optimization; Convex hull BibRef

Lee, K.Y.[Ki-Young], Kim, D.W.[Dae-Won], Lee, D.[Doheon], Lee, K.H.[Kwang H.],
Improving support vector data description using local density degree,
PR(38), No. 10, October 2005, pp. 1768-1771.
WWW Link. 0508
BibRef

Asharaf, S., Shevade, S.K., Murty, M.N.[M. Narasimha],
Rough support vector clustering,
PR(38), No. 10, October 2005, pp. 1779-1783.
WWW Link. 0508
See also Rough set based incremental clustering of interval data. BibRef

Reddy, I.S.[I. Sathish], Shevade, S.K.[Shirish K.], Murty, M.N.,
A fast quasi-Newton method for semi-supervised SVM,
PR(44), No. 10-11, October-November 2011, pp. 2305-2313.
Elsevier DOI 1101
Semi-supervised learning; Support vector machines; Quasi-Newton methods; Nonconvex optimization BibRef

Asharaf, S., Murty, M.N.[M. Narasimha],
Scalable non-linear Support Vector Machine using hierarchical clustering,
ICPR06(I: 908-911).
IEEE DOI 0609
BibRef

Nath, J.S.[J. Saketha], Shevade, S.K.,
An efficient clustering scheme using support vector methods,
PR(39), No. 8, August 2006, pp. 1473-1480.
WWW Link. Clustering; Support vector machines; R*-tree 0606
BibRef

El-Yaniv, R.[Ran], Gerzon, L.[Leonid],
Effective transductive learning via objective model selection,
PRL(26), No. 13, 1 October 2005, pp. 2104-2115.
WWW Link. 0509
BibRef

Lauer, F.[Fabien], Bloch, G.[Gérard],
Ho-Kashyap classifier with early stopping for regularization,
PRL(27), No. 9, July 2006, pp. 1037-1044.
WWW Link. 0605
Early stopping; Robustness; SVM See also Algorithm for Linear Inequalities and its Applications, An. BibRef

Li, M.K.[Ming-Kun], Sethi, I.K.[Ishwar K.],
Confidence-Based Active Learning,
PAMI(28), No. 8, August 2006, pp. 1251-1261.
IEEE DOI 0606
Identify the uncertain samples. BibRef

Li, M.K.[Ming-Kun], Sethi, I.K.[Ishwar K.],
Confidence-based classifier design,
PR(39), No. 7, July 2006, pp. 1230-1240.
WWW Link. 0606
BibRef
Earlier:
SVM-based classifier design with controlled confidence,
ICPR04(I: 164-167).
IEEE DOI 0409
Confidence-based classification; Error estimation; Reject option; Dynamic bin width allocation BibRef

Liu, Y.G.[Yi-Guang], You, Z.S.[Zhi-Sheng], Cao, L.P.[Li-Ping],
A novel and quick SVM-based multi-class classifier,
PR(39), No. 11, November 2006, pp. 2258-2264.
WWW Link. 0608
Multi-class classifier; SVMlight approach; Objective function BibRef

Li, Q.[Qing], Jiao, L.C.[Li-Cheng], Hao, Y.J.[Ying-Juan],
Adaptive simplification of solution for support vector machine,
PR(40), No. 3, March 2007, pp. 972-980.
WWW Link. 0611
Support vector machine; Simplification; Vector correlation; Feature vector; Regression estimation; Pattern recognition BibRef

Han, Y., Lam, W.[Wai], Ling, C.X.[Charles X.],
Customized Generalization of Support Patterns for Classification,
SMC-B(36), No. 6, December 2006, pp. 1306-1318.
IEEE DOI 0701
BibRef

Jayadeva, Khemchandani, R., Chandra, S.[Suresh],
Twin Support Vector Machines for Pattern Classification,
PAMI(29), No. 5, May 2007, pp. 905-910.
IEEE DOI 0704
A binary SVM classifier that determines two nonparallel planes by solving two related SVM-type problems. BibRef

Khemchandani, R., Goyal, K., Chandra, S.[Suresh],
Twin Support Vector Machine based Regression,
ICAPR15(1-6)
IEEE DOI 1511
quadratic programming BibRef

Jayadeva, Shah, S.[Sameena], Chandra, S.[Suresh],
Kernel Optimization Using a Generalized Eigenvalue Approach,
PReMI09(32-37).
Springer DOI 0912
BibRef

Jayadeva, Shah, S.[Sameena], Chandra, S.[Suresh],
Zero Norm Least Squares Proximal SVR,
PReMI09(38-43).
Springer DOI 0912
BibRef

Qiao, H.[Hong], Wang, Y.G.[Yan-Guo], Zhang, B.[Bo],
A simple decomposition algorithm for support vector machines with polynomial-time convergence,
PR(40), No. 9, September 2007, pp. 2543-2549.
WWW Link. 0705
Support vector machines; Decomposition methods; Convergence; Statistical learning theory; Pattern recognition BibRef

Wang, D.[Di], Zhang, B.[Bo], Zhang, P.[Peng], Qiao, H.[Hong],
An online core vector machine with adaptive MEB adjustment,
PR(43), No. 10, October 2010, pp. 3468-3482.
Elsevier DOI 1007
Minimum enclosing ball; Online classifier; Core vector machine; Support vector machine; Machine learning BibRef

Doumpos, M., Zopounidis, C., Golfinopoulou, V.,
Additive Support Vector Machines for Pattern Classification,
SMC-B(37), No. 3, June 2007, pp. 540-550.
IEEE DOI 0706
BibRef

Chuang, C.C.,
Fuzzy Weighted Support Vector Regression With a Fuzzy Partition,
SMC-B(37), No. 3, June 2007, pp. 630-640.
IEEE DOI 0706
BibRef

Tian, S.F.[Sheng-Feng], Mu, S.M.[Shao-Min], Yin, C.H.[Chuan-Huan],
Length-weighted string kernels for sequence data classification,
PRL(28), No. 13, 1 October 2007, pp. 1651-1656.
WWW Link. 0709
Support vector machine; String kernel; Classification BibRef

Zingman, I.[Igor], Meir, R.[Ron], El-Yaniv, R.[Ran],
Size-density spectra and their application to image classification,
PR(40), No. 12, December 2007, pp. 3336-3348.
WWW Link. 0709
Image classification; Algebraic opening; Density opening; Rank-max opening; Pattern size spectrum; Pattern density spectrum; Pattern size-density spectrum; Size-density signature; Support vector machine BibRef

Bayro-Corrochano, E.[Eduardo], Arana-Daniel, N.[Nancy],
Theory and Applications of Clifford Support Vector Machines,
JMIV(28), No. 1, May 2007, pp. 29-46.
Springer DOI 0710
BibRef

López-González, G., Arana-Daniel, N.[Nancy], Bayro-Corrochano, E.[Eduardo],
Quaternion Support Vector Classifier,
CIARP14(722-729).
Springer DOI 1411
BibRef

Ye, W.[Wang], Huang, S.T.[Shang-Teng],
Reducing the number of sub-classifiers for pairwise multi-category support vector machines,
PRL(28), No. 15, 1 November 2007, pp. 2088-2093.
WWW Link. 0711
SVM; Multi-category classification; Pairwise; Uncertainty sampling BibRef

Zafeiriou, S.P., Tefas, A., Pitas, I.,
Minimum Class Variance Support Vector Machines,
IP(16), No. 10, October 2007, pp. 2551-2564.
IEEE DOI 0711
BibRef

Vretos, N., Tefas, A., Pitas, I.,
Using robust dispersion estimation in support vector machines,
PR(46), No. 12, 2013, pp. 3441-3451.
Elsevier DOI 1308
Support vector machines BibRef

Zhou, S.S.[Shui-Sheng], Liu, H.W.[Hong-Wei], Zhou, L.H.[Li-Hua], Ye, F.[Feng],
Semismooth Newton support vector machine,
PRL(28), No. 15, 1 November 2007, pp. 2054-2062.
WWW Link. 0711
Support vector machines; Semismooth; Lagrangian dual; Cholesky factorization BibRef

Astorino, A.[Annabella], Fuduli, A.[Antonio],
Nonsmooth Optimization Techniques for Semisupervised Classification,
PAMI(29), No. 12, December 2007, pp. 2135-2142.
IEEE DOI 0711
Transductive Support Vector Machine. BibRef

Guo, G.[Gao], Zhang, J.S.[Jiang-She],
Reducing examples to accelerate support vector regression,
PRL(28), No. 16, December 2007, pp. 2173-2183.
WWW Link. 0711
Support vector machine; Support vector regression; Data reduced method; Cross validation; k-Nearest neighbor BibRef

Kang, W.S.[Woo-Sung], Choi, J.Y.[Jin Young],
Domain density description for multiclass pattern classification with reduced computational load,
PR(41), No. 6, June 2008, pp. 1997-2009.
WWW Link. 0802
Multiclass pattern classification; Computational load reduction; Support vector learning BibRef

Li, D.F.[Ding-Fang], Hu, W.C.[Wen-Chao], Xiong, W.[Wei], Yang, J.B.[Jin-Bo],
Fuzzy relevance vector machine for learning from unbalanced data and noise,
PRL(29), No. 9, 1 July 2008, pp. 1175-1181.
WWW Link. 0711
Relevance vector machine; Unbalanced data; Noise; Fuzzy membership; Bayesian inference BibRef

Kumar, M.A.[M. Arun], Gopal, M.,
Application of smoothing technique on twin support vector machines,
PRL(29), No. 13, 1 October 2008, pp. 1842-1848.
WWW Link. 0804
Support vector machines; Pattern recognition; Twin support vector machines BibRef

Kumar, M.A.[M. Arun], Gopal, M.,
A comparison study on multiple binary-class SVM methods for unilabel text categorization,
PRL(31), No. 11, 1 August 2010, pp. 1437-1444.
Elsevier DOI 1008
Multiclass classification; One-against-all; One-against-one; Text categorization; Support vector machines (SVMs) BibRef

Yin, J.S.[Jun-Song], Hu, D.[Dewen], Zhou, Z.T.[Zong-Tan],
Noisy manifold learning using neighborhood smoothing embedding,
PRL(29), No. 11, 1 August 2008, pp. 1613-1620.
WWW Link. 0804
Neighbor smoothing embedding (NSE); Manifold learning; Locally linear embedding (LLE); Local linear surface estimator BibRef

Guo, S.M., Chen, L.C., Tsai, J.S.H.,
A boundary method for outlier detection based on support vector domain description,
PR(42), No. 1, January 2009, pp. 77-83.
WWW Link. 0809
Outlier detection; Support vector domain description BibRef

Wang, L., Xue, P., Chan, K.L.,
Two Criteria for Model Selection in Multiclass Support Vector Machines,
SMC-B(38), No. 6, December 2008, pp. 1432-1448.
IEEE DOI 0812
BibRef

Wu, K.P.[Kuo-Ping], Wang, S.D.[Sheng-De],
Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space,
PR(42), No. 5, May 2009, pp. 710-717.
Elsevier DOI 0902
SVM; Support vector machines; Kernel parameters; Inter-cluster distances BibRef

Zhao, Y.P.[Yong-Ping], Sun, J.G.[Jian-Guo],
Recursive reduced least squares support vector regression,
PR(42), No. 5, May 2009, pp. 837-842.
Elsevier DOI 0902
Least squares support vector regression; Reduced technique; Iterative strategy; Parsimoniousness; Classification BibRef

Filippi, A.M., Archibald, R.,
Support Vector Machine-Based Endmember Extraction,
GeoRS(47), No. 3, March 2009, pp. 771-791.
IEEE DOI 0903
BibRef

Liang, X.[Xun], Wang, C.[Chao],
Separating hypersurfaces of SVMs in input spaces,
PRL(30), No. 5, 1 April 2009, pp. 469-476.
Elsevier DOI 0903
Separating hyperplane; Separating hypersurface; Input sample space; High-dimensional feature space; Support vector machine BibRef

Mu, T., Nandi, A.K.[Asoke K.],
Multiclass Classification Based on Extended Support Vector Data Description,
SMC-B(39), No. 5, October 2009, pp. 1206-1216.
IEEE DOI 0906
BibRef

Chen, G.Y.[Guang-Yi], Dudek, G.[Gregory],
Auto-correlation wavelet support vector machine,
IVC(27), No. 8, 2 July 2009, pp. 1040-1046.
Elsevier DOI 0906
BibRef
Earlier:
Auto-Correlation Wavelet Support Vector Machine and Its Applications to Regression,
CRV05(246-252).
IEEE DOI 0505
Wavelets; Support vector machine; Machine learning; Pattern recognition; Function regression; Auto-correlation BibRef

Chen, J., Wang, C., Wang, R.,
Using Stacked Generalization to Combine SVMs in Magnitude and Shape Feature Spaces for Classification of Hyperspectral Data,
GeoRS(47), No. 7, July 2009, pp. 2193-2205.
IEEE DOI 0906
BibRef

Peleg, D.[Dori], Meir, R.[Ron],
A sparsity driven kernel machine based on minimizing a generalization error bound,
PR(42), No. 11, November 2009, pp. 2607-2614.
Elsevier DOI 0907
Sparsity; Classification; Generalization error bounds; Statistical learning theory BibRef

Tuia, D., Pacifici, F.[Fabio], Kanevski, M., Emery, W.J.[William J.],
Classification of Very High Spatial Resolution Imagery Using Mathematical Morphology and Support Vector Machines,
GeoRS(47), No. 11, November 2009, pp. 3866-3879.
IEEE DOI 0911
See also Comparing Statistical and Neural Network Methods Applied to Very High Resolution Satellite Images Showing Changes in Man-Made Structures at Rocky Flats. BibRef

Zhao, W.[Wenzhi], Du, S.H.[Shi-Hong], Wang, Q.[Qiao], Emery, W.J.[William J.],
Contextually guided very-high-resolution imagery classification with semantic segments,
PandRS(132), No. 1, 2017, pp. 48-60.
Elsevier DOI 1710
VHR, images BibRef

Tuia, D., Camps-Valls, G., Matasci, G., Kanevski, M.,
Learning Relevant Image Features with Multiple-Kernel Classification,
GeoRS(48), No. 10, October 2010, pp. 3780-3791.
IEEE DOI 1003
BibRef

Volpi, M., Tuia, D., Kanevski, M.,
Memory-Based Cluster Sampling for Remote Sensing Image Classification,
GeoRS(50), No. 8, August 2012, pp. 3096-3106.
IEEE DOI 1208
BibRef

Matasci, G., Volpi, M., Kanevski, M., Bruzzone, L., Tuia, D.,
Semisupervised Transfer Component Analysis for Domain Adaptation in Remote Sensing Image Classification,
GeoRS(53), No. 7, July 2015, pp. 3550-3564.
IEEE DOI 1503
Feature extraction BibRef

Huang, X., Shi, L., Suykens, J.A.K.[Johan A.K.],
Support Vector Machine Classifier With Pinball Loss,
PAMI(36), No. 5, May 2014, pp. 984-997.
IEEE DOI 1405
Fasteners BibRef

Deselaers, T.[Thomas], Heigold, G.[Georg], Ney, H.[Hermann],
Object classification by fusing SVMs and Gaussian mixtures,
PR(43), No. 7, July 2010, pp. 2476-2484.
Elsevier DOI 1003
BibRef
Earlier:
SVMs, Gaussian mixtures, and their generative/discriminative fusion,
ICPR08(1-4).
IEEE DOI 0812
Support vector machine; Gaussian mixtures; Discriminative classifiers; Generative classifiers; Local-feature-based object recognition BibRef

Weyand, T.[Tobias], Deselaers, T.[Thomas], Ney, H.[Hermann],
Log-linear Mixtures for Object Class Recognition,
BMVC09(xx-yy).
PDF File. 0909
BibRef

Muñoz, A.[Alberto], González, J.[Javier],
Representing Functional Data Using Support Vector Machines,
PRL(31), No. 6, 15 April 2010, pp. 511-516.
Elsevier DOI 1004
BibRef
Earlier: A2, A1: CIARP08(332-339).
Springer DOI 0809
Functional Data Analysis (FDA); Kernel methods; Support vector machines; Cluster; Classification BibRef

Muñoz, A.[Alberto], González, J.[Javier], de Diego, I.M.[Isaac Martín],
Local Linear Approximation for Kernel Methods: The Railway Kernel,
CIARP06(936-944).
Springer DOI 0611
BibRef

Moguerza, J.M.[Javier M.], Muñoz, A.[Alberto], de Diego, I.M.[Isaac Martín],
Fusion of Gaussian Kernels Within Support Vector Classification,
CIARP06(945-953).
Springer DOI 0611
BibRef

Lin, H.J.[Hwei-Jen], Yeh, J.P.[Jih Pin],
A hybrid optimization strategy for simplifying the solutions of support vector machines,
PRL(31), No. 7, 1 May 2010, pp. 563-571.
Elsevier DOI 1004
Support vector machine; Particle swarm optimization; Genetic algorithm; Optimization; Discriminant function; Hyperplane BibRef

Wang, X.M.[Xiao-Ming], Chung, F.L.[Fu-Lai], Wang, S.T.[Shi-Tong],
On minimum class locality preserving variance support vector machine,
PR(43), No. 8, August 2010, pp. 2753-2762.
Elsevier DOI 1006
Supervised learning; Support vector machine; Minimum class variance support machine; Locality preserving projections BibRef

Glasmachers, T.[Tobias], Igel, C.[Christian],
Maximum Likelihood Model Selection for 1-Norm Soft Margin SVMs with Multiple Parameters,
PAMI(32), No. 8, August 2010, pp. 1522-1528.
IEEE DOI 1007
With few initial points. BibRef

Cevikalp, H.[Hakan],
New clustering algorithms for the support vector machine based hierarchical classification,
PRL(31), No. 11, 1 August 2010, pp. 1285-1291.
Elsevier DOI 1008
Hierarchical classification; Support vector machines; Multi-class classification; Clustering; Normalized cuts BibRef

Saha, S.K.[Sujan Kumar], Narayan, S.[Shashi], Sarkar, S.[Sudeshna], Mitra, P.[Pabitra],
A composite kernel for named entity recognition,
PRL(31), No. 12, 1 September 2010, pp. 1591-1597.
Elsevier DOI 1008
Named entity recognition; Support vector machine; Kernel methods; String kernel; Machine learning BibRef

Kumar, M.A.[M. Arun], Gopal, M.,
A hybrid SVM based decision tree,
PR(43), No. 12, December 2010, pp. 3977-3987.
Elsevier DOI 1003
Support vector machines; Decision trees; Hybridization; Pattern recognition BibRef

Giacco, F., Thiel, C., Pugliese, L., Scarpetta, S., Marinaro, M.,
Uncertainty Analysis for the Classification of Multispectral Satellite Images Using SVMs and SOMs,
GeoRS(48), No. 10, October 2010, pp. 3769-3779.
IEEE DOI 1003
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Huang, K.Z.[Kai-Zhu], Zheng, D.N.[Da-Nian], Sun, J.[Jun], Hotta, Y.[Yoshinobu], Fujimoto, K.[Katsuhito], Naoi, S.[Satoshi],
Sparse learning for support vector classification,
PRL(31), No. 13, 1 October 2010, pp. 1944-1951.
Elsevier DOI 1003
Sparse representation; Implementations of L0-norm; Regularization term; Support vector machine; Kernel methods BibRef

Ye, Q.[Qiaolin], Zhao, C.X.[Chun-Xia], Ye, N.[Ning], Chen, Y.[Yannan],
Multi-weight vector projection support vector machines,
PRL(31), No. 13, 1 October 2010, pp. 2006-2011.
Elsevier DOI 1003
Generalized eigenvalues; Multi-weight vector; Matrix singularity; Standard eigenvalues; Singular problems BibRef

Ye, Q.[Qiaolin], Ye, N.[Ning], Yin, T.M.[Tong-Ming],
Enhanced multi-weight vector projection support vector machine,
PRL(42), No. 1, 2014, pp. 91-100.
Elsevier DOI 1404
Multiple weight vectors BibRef

Bovolo, F., Bruzzone, L., Carlin, L.,
A Novel Technique for Subpixel Image Classification Based on Support Vector Machine,
IP(19), No. 11, November 2010, pp. 2983-2999.
IEEE DOI 1011
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Ertekin, S.[Seyda], Bottou, L.[Leon], Giles, C.L.[C. Lee],
Nonconvex Online Support Vector Machines,
PAMI(33), No. 2, February 2011, pp. 368-381.
IEEE DOI 1101
Ramp Loss. Supress influence of outliesrs. BibRef

Han, D.Q.[De-Qiang], Han, C.Z.[Chong-Zhao], Yang, Y.[Yi],
A novel classifier based on shortest feature line segment,
PRL(32), No. 3, 1 February 2011, pp. 485-493.
Elsevier DOI 1101
Nearest feature line (NFL); Trespass inaccuracy; Feature line segment; Geometric relation; Neighborhood-based classifier BibRef

Han, D.Q.[De-Qiang], Han, C.Z.[Chong-Zhao], Yang, Y.[Yi], Liu, Y.[Yu], Mao, W.T.[Wen-Tao],
Pre-extracting method for SVM classification based on the non-parametric K-NN rule,
ICPR08(1-4).
IEEE DOI 0812
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Chang, C.C.[Chih-Cheng], Chien, L.J.[Li-Jen], Lee, Y.J.[Yuh-Jye],
A novel framework for multi-class classification via ternary smooth support vector machine,
PR(44), No. 6, June 2011, pp. 1235-1244.
Elsevier DOI 1102
Confidence; Hidden classes; Multi-class classification; Smooth method; Support vector machine; Ternary voting games BibRef

Guo, L.H.[Li-Hua], Jin, L.W.[Lian-Wen],
Laplacian Support Vector Machines with Multi-Kernel Learning,
IEICE(E94-D), No. 2, February 2011, pp. 379-383.
WWW Link. 1102
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Sahbi, H.[Hichem], Audibert, J.Y.[Jean-Yves], Keriven, R.[Renaud],
Context-Dependent Kernels for Object Classification,
PAMI(33), No. 4, April 2011, pp. 699-708.
IEEE DOI 1103
Not just correlation kernels. BibRef

Sahbi, H.[Hichem], Audibert, J.Y.[Jean-Yves], Rabarisoa, J.[Jaonary], Keriven, R.[Renaud],
Context-dependent kernel design for object matching and recognition,
CVPR08(1-8).
IEEE DOI 0806
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Sahbi, H.[Hichem], Fleuret, F.[François],
Scale-Invariance of Support Vector Machines based on the Triangular Kernel,
INRIARR-4601, Octobre 2002.
HTML Version. 0306
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Sahbi, H.[Hichem], Li, X.[Xi],
Context-Based Support Vector Machines for Interconnected Image Annotation,
ACCV10(I: 214-227).
Springer DOI 1011
Award, ACCV Best Paper. BibRef

Jiu, M., Sahbi, H.[Hichem],
Nonlinear Deep Kernel Learning for Image Annotation,
IP(26), No. 4, April 2017, pp. 1820-1832.
IEEE DOI 1704
Feature extraction BibRef

Veenman, C.J.[Cor J.], Bolck, A.[Annabel],
A sparse nearest mean classifier for high dimensional multi-class problems,
PRL(32), No. 6, 15 April 2011, pp. 854-859.
Elsevier DOI 1103
Classification; Multi-class; Support vector machine; High dimensional; Chemometrics; Bioinformatics BibRef

Ozer, S.[Sedat], Chen, C.H.[Chi H.], Cirpan, H.A.[Hakan A.],
A set of new Chebyshev kernel functions for support vector machine pattern classification,
PR(44), No. 7, July 2011, pp. 1435-1447.
Elsevier DOI 1103
Generalized Chebyshev kernel; Modified Chebyshev kernel; Semi-parametric kernel; Kernel construction BibRef

Wang, Z.[Zheng], Yan, S.C.[Shui-Cheng], Zhang, C.S.[Chang-Shui],
Active learning with adaptive regularization,
PR(44), No. 10-11, October-November 2011, pp. 2375-2383.
Elsevier DOI 1101
Active learning; Adaptive regularization; SVM; TSVM BibRef

Peng, X.J.[Xin-Jun],
TPMSVM: A novel twin parametric-margin support vector machine for pattern recognition,
PR(44), No. 10-11, October-November 2011, pp. 2678-2692.
Elsevier DOI 1101
Support vector machine; Twin support vector machine; Nonparallel hyperplanes; Heteroscedastic noise structure; Parametric-margin model BibRef

Chen, X.B.[Xiao-Bo], Yang, J.[Jian], Ye, Q.L.[Qiao-Lin], Liang, J.[Jun],
Recursive projection twin support vector machine via within-class variance minimization,
PR(44), No. 10-11, October-November 2011, pp. 2643-2655.
Elsevier DOI 1101
Multiple-surface classifier; Twin support vector machine; Quadratic programming BibRef

Wittek, P.[Peter], Tan, C.L.[Chew Lim],
Compactly Supported Basis Functions as Support Vector Kernels for Classification,
PAMI(33), No. 10, October 2011, pp. 2039-2050.
IEEE DOI 1109
Wavelet kernels. Use inner product of kernels. BibRef

Laanaya, H.[Hicham], Abdallah, F.[Fahed], Snoussi, H.[Hichem], Richard, C.[Cédric],
Learning general Gaussian kernel hyperparameters of SVMs using optimization on symmetric positive-definite matrices manifold,
PRL(32), No. 13, 1 October 2011, pp. 1511-1515.
Elsevier DOI 1109
Kernel optimization; Support vector machines; General Gaussian kernel; Symmetric positive-definite matrices manifold BibRef

Li, B.[Bing], Song, S.J.[Shi-Ji], Li, K.[Kang],
Improved conjugate gradient implementation for least squares support vector machines,
PRL(33), No. 2, 15 January 2012, pp. 121-125.
Elsevier DOI 1112
Least square; Support vector machine; Unconstrained optimization; Conjugate gradient method BibRef

Clark, A.R.J.[Andrew R.J.], Everson, R.M.[Richard M.],
Multi-objective learning of Relevance Vector Machine classifiers with multi-resolution kernels,
PR(45), No. 9, September 2012, pp. 3535-3543.
Elsevier DOI 1206
Relevance Vector Machine; Evolutionary algorithm; Classification; Multi-resolution kernels; Cross-validation BibRef

Gkalelis, N., Mezaris, V., Kompatsiaris, I., Stathaki, T.,
Linear Subclass Support Vector Machines,
SPLetters(19), No. 9, September 2012, pp. 575-578.
IEEE DOI 1208
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Wu, J.X.[Jian-Xin],
Efficient HIK SVM Learning for Image Classification,
IP(21), No. 10, October 2012, pp. 4442-4453.
IEEE DOI 1209
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Earlier:
Power mean SVM for large scale visual classification,
CVPR12(2344-2351).
IEEE DOI 1208
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Earlier:
A Fast Dual Method for HIK SVM Learning,
ECCV10(II: 552-565).
Springer DOI 1009
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Wang, C.D.[Chang-Dong], Lai, J.H.[Jian-Huang],
Position regularized Support Vector Domain Description,
PR(46), No. 3, March 2013, pp. 875-884.
Elsevier DOI 1212
Support Vector Domain Description; Weighting; Data clustering; Support vector clustering; SVDD k-Means BibRef

Gonzalez-Abril, L., Velasco, F., Angulo, C., Ortega, J.A.,
A study on output normalization in multiclass SVMs,
PRL(34), No. 3, 1 February 2013, pp. 344-348.
Elsevier DOI 1301
1-v-r SVM; Convex hull; Kernel methods; Multiclassification BibRef

Minoura, K.[Kentaro], Tamura, S.[Satoshi], Hayamizu, S.[Satoru],
Probabilistic expression of Polynomial Semantic Indexing and its application for classification,
PRL(34), No. 13, 2013, pp. 1485-1489.
Elsevier DOI 1308
Polynomial Semantic Indexing BibRef

Abrahamsen, T.J.[Trine Julie], Hansen, L.K.[Lars Kai],
Variance inflation in high dimensional Support Vector Machines,
PRL(34), No. 16, 2013, pp. 2173-2180.
Elsevier DOI 1310
Variance inflation BibRef

Faußer, S.[Stefan], Schwenker, F.[Friedhelm],
Semi-supervised clustering of large data sets with kernel methods,
PRL(37), No. 1, 2014, pp. 78-84.
Elsevier DOI 1402
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Earlier:
Clustering large datasets with kernel methods,
ICPR12(501-504).
WWW Link. 1302
Semi-supervised clustering BibRef

Cheng, Q.A.[Qi-Ang], Tezcan, J.[Jale], Cheng, J.[Jie],
Confidence and prediction intervals for semiparametric mixed-effect least squares support vector machine,
PRL(40), No. 1, 2014, pp. 88-95.
Elsevier DOI 1403
Semiparametric function estimation BibRef

Demir, B.[Begüm], Bruzzone, L.[Lorenzo],
A multiple criteria active learning method for support vector regression,
PR(47), No. 7, 2014, pp. 2558-2567.
Elsevier DOI 1404
Regression BibRef

Demir, B.[Begm], Ertrk, S.[Sarp],
Improving SVM classification accuracy using a hierarchical approach for hyperspectral images,
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IEEE DOI 0911
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Serra-Toro, C.[Carlos], Traver, V.J.[V. Javier], Pla, F.[Filiberto],
Exploring some practical issues of SVM+: Is really privileged information that helps?,
PRL(42), No. 1, 2014, pp. 40-46.
Elsevier DOI 1404
Privileged information BibRef

Chen, W.J.[Wei-Jie], Shao, Y.H.[Yuan-Hai], Li, C.N.[Chun-Na], Deng, N.Y.[Nai-Yang],
MLTSVM: A novel twin support vector machine to multi-label learning,
PR(52), No. 1, 2016, pp. 61-74.
Elsevier DOI 1601
Multi-label classification BibRef

Zhang, H.X.[Hua-Xiang], Cao, L.L.[Lin-Lin], Gao, S.[Shuang],
A locality correlation preserving support vector machine,
PR(47), No. 9, 2014, pp. 3168-3178.
Elsevier DOI 1406
Support vector machine BibRef

Tuia, D., Volpi, M., Dalla Mura, M., Rakotomamonjy, A., Flamary, R.,
Automatic Feature Learning for Spatio-Spectral Image Classification With Sparse SVM,
GeoRS(52), No. 10, October 2014, pp. 6062-6074.
IEEE DOI 1407
Feature extraction BibRef

Nasiri, J.A.[Jalal A.], Charkari, N.M.[Nasrollah Moghadam], Jalili, S.[Saeed],
Least squares twin multi-class classification support vector machine,
PR(48), No. 3, 2015, pp. 984-992.
Elsevier DOI 1412
Twin support vector machine BibRef

Peng, S.[Shili], Hu, Q.H.[Qing-Hua], Chen, Y.[Yinli], Dang, J.[Jianwu],
Improved support vector machine algorithm for heterogeneous data,
PR(48), No. 6, 2015, pp. 2072-2083.
Elsevier DOI 1503
Support vector machine BibRef

Chen, J.H.[Jin-Hui], Takiguchi, T.[Tetsuya], Ariki, Y.[Yasuo],
A robust SVM classification framework using PSM for multi-class recognition,
JIVP(2015), No. 1, 2015, pp. 7.
DOI Link 1503
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Multithreading AdaBoost framework for object recognition,
ICIP15(1235-1239)
IEEE DOI 1512
AUC; Ri-HOG; multithreading AdaBoost BibRef

Chen, J.H.[Jin-Hui], Kitano, Y.[Yosuke], Li, Y.T.[Yi-Ting], Takiguchi, T.[Tetsuya], Ariki, Y.[Yasuo],
A Robust Learning Framework Using PSM and Ameliorated SVMs for Emotional Recognition,
CV4AC14(629-643).
Springer DOI 1504
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Xu, J., Tang, Y.Y.[Yuan Yan], Zou, B.[Bin], Xu, Z.B.[Zong-Ben], Li, L.Q.[Luo-Qing], Lu, Y.[Yang], Zhang, B.,
The Generalization Ability of SVM Classification Based on Markov Sampling,
Cyber(45), No. 6, June 2015, pp. 1169-1179.
IEEE DOI 1506
Cybernetics BibRef

Li, Y.[Ya], Tian, X.M.[Xin-Mei], Song, M.L.[Ming-Li], Tao, D.C.[Da-Cheng],
Multi-task proximal support vector machine,
PR(48), No. 10, 2015, pp. 3249-3257.
Elsevier DOI 1507
Multi-task learning BibRef

Bae, J.S.[Ji-Sang], Kim, J.O.[Jong-Ok],
Multiclass Probabilistic Classification for Support Vector Machines,
IEICE(E98-D), No. 6, June 2015, pp. 1251-1255.
WWW Link. 1505
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Zheng, S.F.[Song-Feng],
Smoothly approximated support vector domain description,
PR(49), No. 1, 2016, pp. 55-64.
Elsevier DOI 1511
Support vector domain description BibRef

Zhang, X.F.[Xue-Feng], Chen, B.[Bo], Liu, H.W.[Hong-Wei], Zuo, L.[Lei], Feng, B.[Bo],
Infinite max-margin factor analysis via data augmentation,
PR(52), No. 1, 2016, pp. 17-32.
Elsevier DOI 1601
Latent variable support vector machine BibRef

Ferreira, M.R.P.[Marcelo R.P.], de Carvalho, F.A.T.[Francisco A.T.], Simões, E.C.[Eduardo C.],
Kernel-based hard clustering methods with kernelization of the metric and automatic weighting of the variables,
PR(51), No. 1, 2016, pp. 310-321.
Elsevier DOI 1601
Kernel clustering BibRef

Osadchy, M.[Margarita], Keren, D.[Daniel], Raviv, D.,
Recognition Using Hybrid Classifiers,
PAMI(38), No. 4, April 2016, pp. 759-771.
IEEE DOI 1603
Computational complexity BibRef

Osadchy, M.[Margarita], Keren, D.[Daniel], Fadida-Specktor, B.[Bella],
Hybrid Classifiers for Object Classification with a Rich Background,
ECCV12(V: 284-297).
Springer DOI 1210
treat the non-object as background. BibRef

Osadchy, M.[Margarita], Keren, D.[Daniel],
Incorporating the Boltzmann Prior in Object Detection Using SVM,
CVPR06(II: 2095-2101).
IEEE DOI 0606
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Samat, A.[Alim], Gamba, P.[Paolo], Abuduwaili, J.[Jilili], Liu, S.C.[Si-Cong], Miao, Z.[Zelang],
Geodesic Flow Kernel Support Vector Machine for Hyperspectral Image Classification by Unsupervised Subspace Feature Transfer,
RS(8), No. 3, 2016, pp. 234.
DOI Link 1604
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Moghaddam, V.H.[Vahid Hooshmand], Hamidzadeh, J.[Javad],
New Hermite orthogonal polynomial kernel and combined kernels in Support Vector Machine classifier,
PR(60), No. 1, 2016, pp. 921-935.
Elsevier DOI 1609
Support Vector Machine (SVM) BibRef

Ji, Y.S.[Ying-Sheng], Chen, Y.S.[Yu-Shu], Fu, H.H.[Hao-Huan], Yang, G.W.[Guang-Wen],
An EnKF-based scheme to optimize hyper-parameters and features for SVM classifier,
PR(62), No. 1, 2017, pp. 202-213.
Elsevier DOI 1705
EnKF BibRef

Xu, G.[Guibiao], Cao, Z.[Zheng], Hu, B.G.[Bao-Gang], Principe, J.C.[Jose C.],
Robust support vector machines based on the rescaled hinge loss function,
PR(63), No. 1, 2017, pp. 139-148.
Elsevier DOI 1612
Support vector machine BibRef

Cheng, F.Y.[Fan-Yong], Zhang, J.[Jing], Li, Z.Y.[Zuo-Yong], Tang, M.Z.[Ming-Zhu],
Double distribution support vector machine,
PRL(88), No. 1, 2017, pp. 20-25.
Elsevier DOI 1703
Minimum margin BibRef

Lin, L.[Liang], Wang, G.R.[Guang-Run], Zuo, W.M.[Wang-Meng], Feng, X.C.[Xiang-Chu], Zhang, L.[Lei],
Cross-Domain Visual Matching via Generalized Similarity Measure and Feature Learning,
PAMI(39), No. 6, June 2017, pp. 1089-1102.
IEEE DOI 1705
Euclidean distance, Face, Neural networks, Pattern matching, Videos, Visualization, Similarity model, cross-domain matching, deep learning, person, verification BibRef

Gu, B.[Bin], Sheng, V.S.[Victor S.], Tay, K.Y.[Keng Yeow], Romano, W.[Walter], Li, S.[Shuo],
Cross Validation Through Two-Dimensional Solution Surface for Cost-Sensitive SVM,
PAMI(39), No. 6, June 2017, pp. 1103-1121.
IEEE DOI 1705
Computational modeling, Fasteners, Kernel, Search methods, Space exploration, Support vector machines, Training, Solution surface, cost-sensitive support vector machine, cross validation, solution path, space, partition BibRef

Zhang, X.[Xin], Zhang, X.[Xiu],
Adaptive multiclass support vector machine for multimodal data analysis,
PR(70), No. 1, 2017, pp. 177-184.
Elsevier DOI 1706
Artificial bee colony BibRef

Maggu, J.[Jyoti], Majumdar, A.[Angshul],
Kernel transform learning,
PRL(98), No. 1, 2017, pp. 117-122.
Elsevier DOI 1710
Transform learning BibRef

Yan, H.[He], Ye, Q.L.[Qiao-Lin], Zhang, T.A.[Tian-An], Yu, D.J.[Dong-Jun], Yuan, X.[Xia], Xu, Y.Q.[Yi-Qing], Fu, L.Y.[Li-Yong],
Least squares twin bounded support vector machines based on L1-norm distance metric for classification,
PR(74), No. 1, 2018, pp. 434-447.
Elsevier DOI 1711
L1-LSTBSVM BibRef


Nguyen, T.D., Nguyen, V., Le, T., Phung, D.,
Distributed data augmented support vector machine on Spark,
ICPR16(498-503)
IEEE DOI 1705
Data models, Distributed databases, Estimation, Industries, Scalability, Sparks, Support vector machines, Apache Spark, big data, distributed computing, large-scale classification, support, vector, machine BibRef

Huang, D., Wang, C.D., Lai, J.H., Liang, Y., Bian, S., Chen, Y.,
Ensemble-driven support vector clustering: From ensemble learning to automatic parameter estimation,
ICPR16(444-449)
IEEE DOI 1705
Clustering algorithms, Kernel, Labeling, Parameter estimation, Shape, Static VAr compensators, Support vector machines BibRef

Aliquintuy, M.[Marcelo], Frandi, E.[Emanuele], Ñanculef, R.[Ricardo], Suykens, J.A.K.[Johan A. K.],
Efficient Sparse Approximation of Support Vector Machines Solving a Kernel Lasso,
CIARP16(208-216).
Springer DOI 1703
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Riera, C.R.[Carles R.], Pujol, O.[Oriol],
An Approximate Support Vector Machines Solver with Budget Control,
CIARP16(377-384).
Springer DOI 1703
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Trichet, R.[Remi], O'Connor, N.E.[Noel E.],
A flexible ensemble-SVM for computer vision tasks,
AVSS16(51-58)
IEEE DOI 1611
Bagging BibRef

Elhoseiny, M.[Mohamed], Elgammal, A.M.[Ahmed M.],
Overlapping Domain Cover for Scalable and Accurate Regression Kernel Machines,
BMVC15(xx-yy).
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Debnath, S.[Subhabrata], Banerjee, A.[Anjan], Namboodiri, V.[Vinay],
Adapting RANSAC SVM to Detect Outliers for Robust Classification,
BMVC15(xx-yy).
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Peters, E.[Ethan], Savakis, A.[Andreas],
SVM with OpenCL: High performance implementation of support vector machines on heterogeneous systems,
ICIP15(4322-4326)
IEEE DOI 1512
GPU Acceleration BibRef

Shi, X.S.[Xiao-Shuang], Guo, Z.H.[Zhen-Hua], Yang, Y.J.[Yu-Jiu], Yang, L.[Lin],
Within-class penalty based multi-class support vector machine,
ICIP15(2746-2750)
IEEE DOI 1512
SVM; multi-class; within-class scatter BibRef

Lu, S.X.[Shu-Xia], Tian, R.[Runa], Zhang, Y.F.[Yu-Fen],
A weighted least squares support vector machine based on covariance matrix,
ICWAPR15(192-197)
IEEE DOI 1511
covariance matrices BibRef

Reininghaus, J.[Jan], Huber, S.[Stefan], Bauer, U.[Ulrich], Kwitt, R.[Roland],
A stable multi-scale kernel for topological machine learning,
CVPR15(4741-4748)
IEEE DOI 1510
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Kobayashi, T.[Takumi],
Three viewpoints toward exemplar SVM,
CVPR15(2765-2773)
IEEE DOI 1510
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Zepeda, J.[Joaquin], Perez, P.[Patrick],
Exemplar SVMs as visual feature encoders,
CVPR15(3052-3060)
IEEE DOI 1510
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Modolo, D.[Davide], Vezhnevets, A.[Alexander], Russakovsky, O.[Olga], Ferrari, V.[Vittorio],
Joint calibration of Ensemble of Exemplar SVMs,
CVPR15(3955-3963)
IEEE DOI 1510
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Shen, B.[Bin], Liu, B.D.[Bao-Di], Allebach, J.P.[Jan P.],
TISVM: Large margin classifier for misaligned image classification,
ICIP14(4251-4255)
IEEE DOI 1502
Computer vision BibRef

Zhang, G.[Guopeng], Piccardi, M.[Massimo],
Sequential Labeling with Structural SVM Under an Average Precision Loss,
SSSPR16(344-354).
Springer DOI 1611
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Earlier:
Sequential labeling with structural SVM under the F1 loss,
ICIP14(5272-5276)
IEEE DOI 1502
Accuracy BibRef

Liu, S.C.[Shu-Chun], Guo, J.[Jun], Zhong, S.Z.[Si-Zhi], Li, Y.F.[Yi-Fan],
A Novel Robust Modified Support Vector Machines,
ICPR14(3834-3838)
IEEE DOI 1412
Classification algorithms BibRef

Raval, N.[Nisarg], Tonge, R.[Rashmi], Jawahar, C.V.,
Efficient Evaluation of SVM Classifiers Using Error Space Encoding,
ICPR14(4411-4416)
IEEE DOI 1412
Accuracy BibRef

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Randomized Support Vector Forest,
BMVC14(xx-yy).
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Nour-Eddine, L.[Lachachi], Abdelkader, A.[Adla],
Reduced Data Based Improved MEB/L2-SVM Equivalence,
MCPR14(1-10).
Springer DOI 1407
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Long, C.J.[Cheng-Jiang], Hua, G.[Gang],
Multi-class Multi-annotator Active Learning with Robust Gaussian Process for Visual Recognition,
ICCV15(2839-2847)
IEEE DOI 1602
Bayes methods See also Joint Gaussian Process Model for Active Visual Recognition with Expertise Estimation in Crowdsourcing, A. BibRef

Hua, G.[Gang], Long, C.J.[Cheng-Jiang], Yang, M.[Ming], Gao, Y.[Yan],
Collaborative Active Learning of a Kernel Machine Ensemble for Recognition,
ICCV13(1209-1216)
IEEE DOI 1403
Active Learning BibRef

Sharma, G.[Gaurav], Jurie, F.[Frederic],
A Novel Approach for Efficient SVM Classification with Histogram Intersection Kernel,
BMVC13(xx-yy).
DOI Link 1402
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Shao, Y.H.[Yuan-Hai], Deng, N.Y.[Nai-Yang], Chen, W.J.[Wei-Jie], Wang, Z.[Zhen],
Improved Generalized Eigenvalue Proximal Support Vector Machine,
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IEEE DOI 1303
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Ren, Y.M.[Yue-Mei], Zhang, Y.N.[Yan-Ning], Meng, Q.J.[Qing-Jie], Zhang, L.[Lei],
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ICPR12(2274-2277).
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Seredin, O.[Oleg], Mottl, V.[Vadim], Tatarchuk, A.[Alexander], Razin, N.[Nikolay], Windridge, D.[David],
Convex support and Relevance Vector Machines for selective multimodal pattern recognition,
ICPR12(1647-1650).
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Xue, H.[Hui], Chen, S.C.[Song-Can], Huang, J.J.[Ji-Jian],
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ICPR12(497-500).
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Ji, Y.[You], Sun, S.L.[Shi-Liang], Lu, Y.[Yue],
Multitask multiclass privileged information support vector machines,
ICPR12(2323-2326).
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Litayem, S.[Saloua], Joly, A.[Alexis], Boujemaa, N.[Nozha],
Hash-Based Support Vector Machines Approximation for Large Scale Prediction,
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Darkner, S.[Sune], Clemmensen, L.H.[Line H.],
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MLMI12(70-77).
Springer DOI 1211
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Bazavan, E.G.[Eduard Gabriel], Li, F.X.[Fu-Xin], Sminchisescu, C.[Cristian],
Fourier Kernel Learning,
ECCV12(II: 459-473).
Springer DOI 1210
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González, F.A.[Fabio A.], Bermeo, D.[David], Ramos, L.[Laura], Nasraoui, O.[Olfa],
On the Robustness of Kernel-based Clustering,
CIARP12(122-129).
Springer DOI 1209
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Cohen, D.A.[Diego Arab], Fernández, E.A.[Elmer Andrés],
SVMTOCP: A Binary Tree Base SVM Approach through Optimal Multi-class Binarization,
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Springer DOI 1209
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Porro-Muñoz, D.[Diana], Duin, R.P.W.[Robert P.W.], Talavera, I.[Isneri],
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Springer DOI 1311
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Hernández, N.[Noslen], Biscay, R.J.[Rolando J.], Talavera, I.[Isneri],
A Non Bayesian Predictive Approach for Functional Calibration,
CIARP12(781-788).
Springer DOI 1209
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Earlier:
Support Vector Regression Methods for Functional Data,
CIARP07(564-573).
Springer DOI 0711
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Hernández, N.[Noslen], Biscay, R.J.[Rolando J.], Villa-Vialaneix, N.[Nathalie], Talavera, I.[Isneri],
A Functional Density-Based Nonparametric Approach for Statistical Calibration,
CIARP10(450-457).
Springer DOI 1011
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Veon, K.L.[Kevin L.], Mahoor, M.H.[Mohammad H.],
Localized support vector machines using Parzen window for incomplete sets of categories,
WACV11(448-454).
IEEE DOI 1101
Deal with objects that are undefined. BibRef

Wu, J.[Jun], Lin, Z.K.[Zheng-Kui], Lu, M.Y.[Ming-Yu],
Asymmetric semi-supervised boosting for SVM active learning in CBIR,
CIVR10(182-188).
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Gripton, A.[Adam], Lu, W.P.[Wei-Ping],
Kernel Domain Description with Incomplete Data: Using Instance-Specific Margins to Avoid Imputation,
ICPR10(2921-2924).
IEEE DOI 1008
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Gao, J.[Jun], Hu, W.M.[Wei-Ming], Li, W.[Wei], Zhang, Z.F.M.[Zhong-Fei Mark], Wu, O.[Ou],
Local Outlier Detection Based on Kernel Regression,
ICPR10(585-588).
IEEE DOI 1008
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He, H.[He], Ghodsi, A.[Ali],
Rare Class Classification by Support Vector Machine,
ICPR10(548-551).
IEEE DOI 1008
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Li, J.[Jinbo], Sun, S.[Shiliang],
Nonlinear Combination of Multiple Kernels for Support Vector Machines,
ICPR10(2889-2892).
IEEE DOI 1008
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Wu, J.[Jun], Lu, M.Y.[Ming-Yu], Wang, C.L.[Chun-Li],
Enhancing SVM Active Learning for Image Retrieval Using Semi-supervised Bias-Ensemble,
ICPR10(3175-3178).
IEEE DOI 1008
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Khan, N.M.[Naimul Mefraz], Ksantini, R.[Riadh], Ahmad, I.S.[Imran Shafiq], Boufama, B.[Boubaker],
A New SVM + NDA Model for Improved Classification and Recognition,
ICIAR10(I: 127-136).
Springer DOI 1006
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Chen, S.[Shuo], Zhang, C.S.[Chang-Shui],
Image classification via SVM using in-between universum samples,
ICIP09(1421-1424).
IEEE DOI 0911
I.e. samples that do not belong to any task related classes. BibRef

Banki, M.H.[Mohammad Hossein], Shirazi, A.A.B.[Ali Asghar Beheshti],
Using Wavelet Support Vector Machine for Classification of Hyperspectral Images,
ICMV09(154-157).
IEEE DOI 0912
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Kim, J.[Junae], Shen, C.H.[Chun-Hua], Wang, L.[Lei],
Learning Cascaded Reduced-Set SVMs Using Linear Programming,
DICTA08(619-626).
IEEE DOI 0812
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Sun, Z.C.[Zhi-Chao], Liu, Z.G.[Zhi-Gang], Liu, S.H.[Su-Hong], Zhang, Y.[Yun], Yang, B.[Bing],
Active Learning with Support Vector Machines in Remotely Sensed Image Classification,
CISP09(1-6).
IEEE DOI 0910
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Hai-Yuan, L.[Liu], Sun, J.C.[Jian-Cheng],
A Modulation Type Recognition Method Using Wavelet Support Vector Machines,
CISP09(1-4).
IEEE DOI 0910
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Nowozin, S.[Sebastian], Gehler, P.V.[Peter V.], Lampert, C.H.[Christoph H.],
On Parameter Learning in CRF-Based Approaches to Object Class Image Segmentation,
ECCV10(VI: 98-111).
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Gehler, P.V.[Peter V.], Nowozin, S.[Sebastian],
On Feature Combination for Multiclass Object Classification,
ICCV09(221-228).
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Let the kernel figure it out; Principled learning of pre-processing for kernel classifiers,
CVPR09(2836-2843).
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Basak, J.[Jayanta],
A least square kernel machine with box constraints,
ICPR08(1-4).
IEEE DOI 0812
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Alpcan, T.[Tansu], Bauckhage, C.[Christian],
A discrete-time parallel update algorithm for distributed learning,
ICPR08(1-4).
IEEE DOI 0812
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Ilayaraja, P., Neeba, N.V., Jawahar, C.V.,
Efficient implementation of SVM for large class problems,
ICPR08(1-4).
IEEE DOI 0812
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Nishida, K.[Kenji], Fujiki, J.[Jun], Kurita, T.[Takio],
Multiple Random Subset-Kernel Learning,
CAIP11(I: 343-350).
Springer DOI 1109
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Nishida, K.[Kenji], Kurita, T.[Takio],
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ICPR08(1-4).
IEEE DOI 0812
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Tatarchuk, A., Mottl, V., Eliseyev, A., Windridge, D.,
Selectivity supervision in combining pattern-recognition modalities by feature- and kernel-selective support vector machines,
ICPR08(1-4).
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Mao, W.T.[Wen-Tao], Dong, L.L.[Long-Lei], Zhang, G.[Gang],
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ICPR08(1-4).
IEEE DOI 0812
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Ozer, S.[Sedat], Chen, C.H.[Chi Hau],
Generalized Chebyshev Kernels for Support Vector Classification,
ICPR08(1-4).
IEEE DOI 0812
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Yu, X.D.[Xiao-Dong], DeMenthon, D.F.[Daniel F.], Doermann, D.[David],
Support Vector Data Description for image categorization from Internet images,
ICPR08(1-4).
IEEE DOI 0812
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Lienemann, K.[Kai], Plotz, T.[Thomas], Fink, G.A.[Gernot A.],
SVM ensemble classification of NMR spectra based on different configurations of data processing techniques,
ICPR08(1-4).
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Habib, T.[Tarek], Inglada, J.[Jordi], Mercier, G.[Gregoire], Chanussot, J.[Jocelyn],
Speeding up Support Vector Machine (SVM) image classification by a kernel series expansion,
ICIP08(865-868).
IEEE DOI 0810
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Sadeghi, M.T.[Mohammad T.], Samiei, M.[Masoumeh], Kittler, J.V.[Josef V.],
Selection and Fusion of Similarity Measure Based Classifiers Using Support Vector Machines,
SSPR08(479-488).
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Schnitzspan, P.[Paul], Fritz, M.[Mario], Roth, S.[Stefan], Schiele, B.[Bernt],
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CVPR09(2238-2245).
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Demirkesen, C.[Can], Cherifi, H.[Hocine],
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ACIVS08(xx-yy).
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Hazan, T.[Tamir], Man, A.[Amit], Shashua, A.[Amnon],
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CVPR08(1-8).
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Maji, S.[Subhransu],
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WebScale12(I: 239-248).
Springer DOI 1210
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Maji, S.[Subhransu], Berg, A.C.[Alexander C.],
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ICCV09(40-47).
IEEE DOI 0909
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Maji, S.[Subhransu], Berg, A.C.[Alexander C.], Malik, J.[Jitendra],
Classification using intersection kernel support vector machines is efficient,
CVPR08(1-8).
IEEE DOI 0806
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Bristow, H.[Hilton], Lucey, S.[Simon],
V1-Inspired Features Induce a Weighted Margin in SVMs,
ECCV12(II: 59-72).
Springer DOI 1210
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Lucey, S.[Simon],
Enforcing non-positive weights for stable support vector tracking,
CVPR08(1-8).
IEEE DOI 0806
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Sluzhivoy, A.[Andrey], Pauli, J.[Josef], Rölke, V.[Volker], Noglik, A.[Anastasia],
Improving the Run-Time Performance of Multi-class Support Vector Machines,
DAGM08(xx-yy).
Springer DOI 0806
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Varma, C.M.B.S.[C.M.B. Seshikanth], Asharaf, S., Murty, M.N.[M. Narasimha],
Rough Core Vector Clustering,
PReMI07(304-310).
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Kong, X.D.[Xiao-Dong], Luo, Q.S.[Qing-Shan], Zeng, G.H.[Gui-Hua],
A Novel SVM-Based Method for Moving Video Objects Recognition,
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ICCV09(1157-1164).
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Support Kernel Machines for Object Recognition,
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IEEE DOI 0710
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Moguerza, J.M.[Javier M.], Muñoz, A.[Alberto], Psarakis, S.[Stelios],
Monitoring Nonlinear Profiles Using Support Vector Machines,
CIARP07(574-583).
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Chatelain, C., Adam, S., Lecourtier, Y., Heutte, L., Paquet, T.,
Multi-Objective Optimization for SVM Model Selection,
ICDAR07(427-431).
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Karim, R.[Rezaul], Bergtholdt, M.[Martin], Kappes, J.H.[Jörg H.], Schnörr, C.[Christoph],
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DAGM07(395-404).
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Tanaka, A.[Akira], Takigawa, I.[Ichigaku], Imai, H.[Hideyuki], Kudo, M.[Mineichi],
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SSSPR14(273-281).
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Earlier:
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SSSPR12(345-353).
Springer DOI 1211
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Tanaka, A.[Akira], Imai, H.[Hideyuki], Kudo, M.[Mineichi], Miyakoshi, M.[Masaaki],
A Relationship Between Generalization Error and Training Samples in Kernel Regressors,
ICPR10(1421-1424).
IEEE DOI 1008
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And:
Optimal Kernel in a Class of Kernels with an Invariant Metric,
SSPR08(530-539).
Springer DOI 0812
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Tanaka, A.[Akira], Sugiyama, M.[Masashi], Imai, H.[Hideyuki], Kudo, M.[Mineichi], Miyakoshi, M.[Masaaki],
Model Selection Using a Class of Kernels with an Invariant Metric,
SSPR06(862-870).
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Joachims, T.[Thorsten],
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SSPR06(1-7).
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Zhang, R., Metaxas, D.,
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IEEE DOI 0609
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Qin, J.Z.[Jian-Zhao], Li, Y.Q.[Yuan-Qing],
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ICPR06(I: 1240-1243).
IEEE DOI 0609
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Chen, G.Y., Bhattacharya, P.,
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ICPR06(II: 614-617).
IEEE DOI 0609
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Ye, N.[Ning], Sun, R.[Ruixiang], Liu, Y.G.[Yin-Gan], Cao, L.[Lin],
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IEEE DOI 0609
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Barakat, N.[Nahla], Bradley, A.P.[Andrew P.],
Rule Extraction from Support Vector Machines: Measuring the Explanation Capability Using the Area under the ROC Curve,
ICPR06(II: 812-815).
IEEE DOI 0609
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Zhang, X.F.[Xian-Fei], Li, B.C.[Bi-Cheng], Shi, W.[Wang], Cheng, L.[Luo],
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IEEE DOI 0609
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Sung, E.[Eric], Yan, Z.[Zhu], Li, X.C.[Xu-Chun],
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IEEE DOI 0609
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Wu, Z.L.[Zhi-Li], Li, C.H.[Chun-Hung], Zhu, J.[Ji], Huang, J.[Jian],
A Semi-supervised SVM for Manifold Learning,
ICPR06(II: 490-493).
IEEE DOI 0609
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Arreola, K.Z.[Karina Zapien], Fehr, J.[Janis], Burkhardt, H.[Hans],
Fast Support Vector Machine Classification using linear SVMs,
ICPR06(III: 366-369).
IEEE DOI 0609
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Brun, A.[Anders], Westin, C.F.[Carl-Fredrik], Herberthson, M.[Magnus], Knutsson, H.[Hans],
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SCIA05(920-929).
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Zhan, Y.Q.[Yi-Qiang], Shen, D.G.[Ding-Gang],
Increasing Efficiency of SVM by Adaptively Penalizing Outliers,
EMMCVPR05(539-551).
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Tung, J.W.[Jia-Wen], Hsu, C.T.[Chiou-Ting],
Learning Hidden Semantic Cues Using Support Vector Clustering,
ICIP05(I: 1189-1192).
IEEE DOI 0512
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Kahsay, L.[Laine], Schwenker, F.[Friedhelm], Palm, G.[Günther],
Comparison of Multiclass SVM Decomposition Schemes for Visual Object Recognition,
DAGM05(334).
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Lyu, S.W.[Si-Wei],
Mercer Kernels for Object Recognition with Local Features,
CVPR05(II: 223-229).
IEEE DOI 0507
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Sun, Q.A.[Qi-Ang], DeJong, G.[Gerald],
Feature Kernel Functions: Improving SVMs Using High-Level Knowledge,
CVPR05(II: 177-183).
IEEE DOI 0507
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ICPR06(II: 808-811).
IEEE DOI 0609
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Boughorbel, S., Tarel, J.P., Boujemaa, N.,
Generalized Histogram Intersection Kernel for Image Recognition,
ICIP05(III: 161-164).
IEEE DOI 0512
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Boughorbel, S., Tarel, J.P., Fleuret, F.,
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BMVC04(xx-yy).
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Wang, Y.C.A.[Yu-Chi-Ang], Casasent, D.,
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IEEE DOI 0508
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Yu, W.M.[Wei Miao], Du, T.H.[Tie-Hua], Lim, K.B.[Kah Bin],
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ICARCV04(II: 1309-1314).
IEEE DOI 0412
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Man, H.[Hong], Chen, L.[Ling], Duan, R.[Rong],
Rotation invariant texture classification using directional filter bank and support vector machine,
ICIP04(III: 1545-1548).
IEEE DOI 0505
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Hein, M.[Matthias], Lal, T.N.[Thomas Navin], Bousquet, O.[Olivier],
Hilbertian Metrics on Probability Measures and Their Application in SVMs,
DAGM04(270-277).
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Horikawa, Y.[Yo],
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ICPR04(I: 660-663).
IEEE DOI 0409
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Park, J.H.[Jin-Hyeong], Ji, X.[Xiang], Zha, H.Y.[Hong-Yuan], Kasturi, R.,
Support vector clustering combined with spectral graph partitioning,
ICPR04(IV: 581-584).
IEEE DOI 0409
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Imbault, F., Lebart, K.,
A stochastic optimization approach for parameter tuning of support vector machines,
ICPR04(IV: 597-600).
IEEE DOI 0409
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Hoi, S.C.H.[Steven C. H.], Lyu, M.R.[Michael R.],
Group-based relevance feedback with support vector machine ensembles,
ICPR04(III: 874-877).
IEEE DOI 0409
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Chen, J.H.[Jiun-Hung],
M-estimator based robust kernels for support vector machines,
ICPR04(I: 168-171).
IEEE DOI 0409
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Gokcen, I., Joachim, D., Deller, J.R.,
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ICPR04(I: 172-175).
IEEE DOI 0409
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Zhang, P.[Peng], Peng, J.[Jing], Riedel, N.,
Discriminant Analysis: A Least Squares Approximation View,
LCV05(III: 46-46).
IEEE DOI 0507
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Zhang, P.[Peng], Peng, J.[Jing],
Efficient Regularized Least Squares Classification,
LCV04(98).
IEEE DOI 0406
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And:
SVM vs regularized least squares classification,
ICPR04(I: 176-179).
IEEE DOI 0409
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Tortorella, F.,
An empirical comparison of in-learning and post-learning optimization schemes for tuning the support vector machines in cost-sensitive applications,
CIAP03(560-566).
IEEE DOI 0310
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Yuan, C.[Chao], Casasent, D.,
A novel support vector classifier with better rejection performance,
CVPR03(I: 419-424).
IEEE DOI 0307
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Vishwanathan, S.V.N., Murty, M.N.,
Geometric SVM: a fast and intuitive SVM algorithm,
ICPR02(II: 56-59).
IEEE DOI 0211
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Xiao, X.[Xipan], Ai, H.Z.[Hai-Zhou], Xu, G.Y.[Guang-You],
Pair-wise sequential reduced set for optimization of support vector machines,
ICPR02(II: 860-863).
IEEE DOI 0211
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Zhang, J., Zhang, Y., Zhou, T.,
Classification of Hyperspectral Data Using Support Vector Machine,
ICIP01(I: 882-885).
IEEE DOI 0108
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Nakamura, E., Murayama, N., Sawada, K., Okuizumi, H.,
RLGS Profile Segmentation Via a SVM,
ICIP01(I: 533-536).
IEEE DOI 0108
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Ben-Hur, A., Horn, D., Siegelmann, H.T., Vapnik, V.,
A Support Vector Clustering Method,
ICPR00(Vol II: 724-727).
IEEE DOI 0009
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Odone, F., Trucco, M., Verri, A.,
Visual Learning of Weight from Shape Using Support Vector Machines,
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Pawlak, M., Ng, M.F.Y.F.[M.F. Yat Fung],
On kernel and radial basis function techniques for classification and function recovering,
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IEEE DOI 9410
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Pawlak, M., Siedlecki, W.,
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ICPR90(I: 677-680).
IEEE DOI 9006
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Training Support Vector Machines, SVM Training, Learning .


Last update:Nov 11, 2017 at 13:31:57