14.2.18.2 Support Vector Machines, SVM, Incremental, Multi-Step

Chapter Contents (Back)
Support Vector Machines. SVM. Recognition.

Lau, K.W., Wu, Q.H.,
Online training of support vector classifier,
PR(36), No. 8, August 2003, pp. 1913-1920.
WWW Link. 0304
BibRef

Lau, K.W., Wu, Q.H.,
Leave one support vector out cross validation for fast estimation of generalization errors,
PR(37), No. 9, September 2004, pp. 1835-1840.
WWW Link. 0407
BibRef

Katagiri, S.[Shinya], Abe, S.[Shigeo],
Incremental training of support vector machines using hyperspheres,
PRL(27), No. 13, 1 October 2006, pp. 1495-1507.
WWW Link. Incremental training; Hyperspheres; 0606
BibRef

Abe, S.[Shigeo],
Fuzzy support vector machines for multilabel classification,
PR(48), No. 6, 2015, pp. 2110-2117.
Elsevier DOI 1503
Multilabel classification BibRef

Cheng, S.X.[Shou-Xian], Shih, F.Y.[Frank Y.],
An improved incremental training algorithm for support vector machines using active query,
PR(40), No. 3, March 2007, pp. 964-971.
WWW Link. 0611
Incremental training; Active learning; Support vector machine; Clustering algorithm; Pattern classification See also Improved feature reduction in input and feature spaces. BibRef

Carvalho, B.P.R., Braga, A.P.,
IP-LSSVM: A two-step sparse classifier,
PRL(30), No. 16, 1 December 2009, pp. 1507-1515.
Elsevier DOI 0911
Sparse classifier; Least squares support vector machine; Support vector automatic detection BibRef

Duan, H.[Hua], Shao, X.J.[Xiao-Jian], Hou, W.Z.[Wei-Zhen], He, G.P.[Guo-Ping], Zeng, Q.T.[Qing-Tian],
An incremental learning algorithm for Lagrangian support vector machines,
PRL(30), No. 15, 1 November 2009, pp. 1384-1391.
Elsevier DOI 0910
Lagrangian; Support vector machines; Incremental learning; Online learning BibRef

Pronobis, A.[Andrzej], Jie, L.[Luo], Caputo, B.[Barbara],
The more you learn, the less you store: Memory-controlled incremental SVM for visual place recognition,
IVC(28), No. 7, July 2010, pp. 1080-1097.
Elsevier DOI 1006
Localization. Incremental learning; Knowledge transfer; Support vector machines; Place recognition; Visual robot localization BibRef

He, X.S.[Xi-Sheng], Wang, Z.[Zhe], Jin, C.[Cheng], Zheng, Y.B.[Ying-Bin], Xue, X.Y.[Xiang-Yang],
A simplified multi-class support vector machine with reduced dual optimization,
PRL(33), No. 1, 1 January 2012, pp. 71-82.
Elsevier DOI 1112
Multi-class classification; Support vector machine; Kernel-based methods; Pattern classification BibRef

Nikitidis, S.[Symeon], Nikolaidis, N.[Nikos], Pitas, I.[Ioannis],
Multiplicative update rules for incremental training of multiclass support vector machines,
PR(45), No. 5, May 2012, pp. 1838-1852.
Elsevier DOI 1201
BibRef
Earlier:
Incremental Training of Multiclass Support Vector Machines,
ICPR10(4267-4270).
IEEE DOI 1008
Support vector machines; Online training; Incremental learning; Quadratic programming; Warm-start algorithm BibRef

Qi, Z.Q.[Zhi-Quan], Tian, Y.J.[Ying-Jie], Shi, Y.[Yong],
Robust twin support vector machine for pattern classification,
PR(46), No. 1, January 2013, pp. 305-316.
Elsevier DOI 1209
Award, Pattern Recognition, Best Paper. Classification; Twin support vector machine; Second order cone programming; Robust BibRef

Tian, Y.J.[Ying-Jie], Qi, Z.Q.[Zhi-Quan], Ju, X., Shi, Y.[Yong], Liu, X.,
Nonparallel Support Vector Machines for Pattern Classification,
Cyber(44), No. 7, July 2014, pp. 1067-1079.
IEEE DOI 1407
Cybernetics BibRef

Chen, D.[Dandan], Tian, Y.J.[Ying-Jie], Liu, X.H.[Xiao-Hui],
Structural nonparallel support vector machine for pattern recognition,
PR(60), No. 1, 2016, pp. 296-305.
Elsevier DOI 1609
Structural information BibRef

Zhao, J.W.[Jin-Wei], Yan, G.R.[Gui-Rong], Feng, B.Q.[Bo-Qin], Mao, W.T.[Wen-Tao], Bai, J.Q.[Jun-Qing],
An adaptive support vector regression based on a new sequence of unified orthogonal polynomials,
PR(46), No. 3, March 2013, pp. 899-913.
Elsevier DOI 1212
Chebyshev polynomials; Kernel function; Adaptable measures; Small sample; Generalization ability BibRef

Ji, Y.[You], Sun, S.[Shiliang],
Multitask multiclass support vector machines: Model and experiments,
PR(46), No. 3, March 2013, pp. 914-924.
Elsevier DOI 1212
Multiclass classification; Multitask learning; Support vector machine; Kernel; Regularization BibRef

Peng, J.X.[Jian-Xun], Ferguson, S.[Stuart], Rafferty, K.[Karen], Stewart, V.[Victoria],
A sequential algorithm for sparse support vector classifiers,
PR(46), No. 4, April 2013, pp. 1195-1208.
Elsevier DOI 1301
Support vector classifier; Sequential algorithm; Sparse design BibRef

Wang, Z.[Zhen], Shao, Y.H.[Yuan-Hai], Wu, T.R.[Tie-Ru],
A GA-based model selection for smooth twin parametric-margin support vector machine,
PR(46), No. 8, August 2013, pp. 2267-2277.
Elsevier DOI 1304
Pattern classification; Support vector machine; Twin support vector machine; Smoothing techniques; Genetic algorithm BibRef

Souza, R.C.S.N.P.[Roberto C.S.N.P.], Leite, S.C.[Saul C.], Borges, C.C.H.[Carlos C.H.], Neto, R.F.[Raul Fonseca],
Online algorithm based on support vectors for orthogonal regression,
PRL(34), No. 12, 1 September 2013, pp. 1394-1404.
Elsevier DOI 1306
Support vector machines; Online algorithms; Kernel methods; Regression problem; Orthogonal regression BibRef

Hou, C.P.[Chen-Ping], Nie, F.P.[Fei-Ping], Zhang, C.S.[Chang-Shui], Yi, D.Y.[Dong-Yun], Wu, Y.[Yi],
Multiple rank multi-linear SVM for matrix data classification,
PR(47), No. 1, 2014, pp. 454-469.
Elsevier DOI 1310
Pattern recognition BibRef

Kim, K.[Kyoungok], Lee, D.W.[Dae-Won],
Inductive manifold learning using structured support vector machine,
PR(47), No. 1, 2014, pp. 470-479.
Elsevier DOI 1310
Dimensionality reduction BibRef

Aytar, Y.[Yusuf], Zisserman, A.[Andrew],
Part level transfer regularization for enhancing exemplar SVMs,
CVIU(138), No. 1, 2015, pp. 114-123.
Elsevier DOI 1506
BibRef
Earlier:
Enhancing Exemplar SVMs using Part Level Transfer Regularization,
BMVC12(79).
DOI Link 1301
BibRef
And:
Multi-Task Multi-Sample Learning,
TASKCV14(78-91).
Springer DOI 1504
Exemplar SVMs BibRef

Wang, D.[Di], Zhang, X.Q.[Xiao-Qin], Fan, M.Y.[Ming-Yu], Ye, X.Z.[Xiu-Zi],
Hierarchical mixing linear support vector machines for nonlinear classification,
PR(59), No. 1, 2016, pp. 255-267.
Elsevier DOI 1609
Support vector machine BibRef


Shapovalova, N.[Nataliya], Mori, G.[Greg],
Clustered Exemplar-SVM: Discovering sub-categories for visual recognition,
ICIP15(93-97)
IEEE DOI 1512
sub-categories; visual recognition BibRef

Xie, W.[Weiyi], Uhlmann, S.[Stefan], Kiranyaz, S.[Serkan], Gabbouj, M.[Moncef],
Incremental Learning with Support Vector Data Description,
ICPR14(3904-3909)
IEEE DOI 1412
Accuracy BibRef

Rosales-Pérez, A.[Alejandro], Gonzalez, J.A.[Jesus A.], Coello-Coello, C.A.[Carlos A.], Reyes-Garcia, C.A.[Carlos A.], Escalante, H.J.[Hugo Jair],
Evolutionary Multi-Objective Approach for Prototype Generation and Feature Selection,
CIARP14(424-431).
Springer DOI 1411
BibRef
Earlier: A1, A5, A2, A4, Only:
Bias and Variance Multi-objective Optimization for Support Vector Machines Model Selection,
IbPRIA13(108-116).
Springer DOI 1307
BibRef

Fefilatyev, S.[Sergiy], Shreve, M.[Matthew], Kramer, K.[Kurt], Hall, L.O.[Lawrence O.], Goldgof, D.B.[Dmitry B.], Kasturi, R.[Rangachar], Daly, K.[Kendra], Remsen, A.[Andrew], Bunke, H.[Horst],
Label-noise reduction with support vector machines,
ICPR12(3504-3508).
WWW Link. 1302
BibRef

Huang, D.[Dong], Lai, J.H.[Jian-Huang], Wang, C.D.[Chang-Dong],
Incremental support vector clustering with outlier detection,
ICPR12(2339-2342).
WWW Link. 1302
BibRef

Zhang, W.Y.[Wei-Yu], Yu, S.X.[Stella X.], Teng, S.H.[Shang-Hua],
Power SVM: Generalization with exemplar classification uncertainty,
CVPR12(2144-2151).
IEEE DOI 1208
BibRef

Han, X.[Xufeng], Berg, A.C.[Alexander C.],
DCMSVM: Distributed parallel training for single-machine multiclass classifiers,
CVPR12(3554-3561).
IEEE DOI 1208
BibRef

Vedaldi, A.[Andrea], Blaschko, M.[Matthew], Zisserman, A.[Andrew],
Learning equivariant structured output SVM regressors,
ICCV11(959-966).
IEEE DOI 1201
BibRef

Zhang, L.H.[Li-He], Zhang, K.Y.[Kun-Yu], Dong, X.L.[Xiao-Li],
Online sparse learning utilizing multi-feature combination for image classification,
ICIP11(197-200).
IEEE DOI 1201
BibRef

Liu, X.B.[Xiao-Bai], Yuan, X.T.[Xiao-Tong], Yan, S.C.[Shui-Cheng], Jin, H.[Hai],
Multi-class semi-supervised SVMs with Positiveness Exclusive Regularization,
ICCV11(1435-1442).
IEEE DOI 1201
BibRef

Kapp, M.N.[Marcelo N.], Sabourin, R.[Robert], Maupin, P.[Patrick],
Adaptive Incremental Learning with an Ensemble of Support Vector Machines,
ICPR10(4048-4051).
IEEE DOI 1008
BibRef

Díaz-Chito, K.[Katerine], Ferri, F.J.[Francesc J.], Díaz-Villanueva, W.[Wladimiro],
Null Space Based Image Recognition Using Incremental Eigendecomposition,
IbPRIA11(313-320).
Springer DOI 1106
BibRef
Earlier:
Image Recognition through Incremental Discriminative Common Vectors,
ACIVS10(II: 304-311).
Springer DOI 1012
BibRef
And:
An Empirical Evaluation of Common Vector Based Classification Methods and Some Extensions,
SSPR08(977-985).
Springer DOI 0812
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Support Vector Machines, SVM, Applied to Recognition .


Last update:Mar 13, 2017 at 16:25:24