14.2.18.1 Training Support Vector Machines, SVM Training, Learning

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

Cao, L.J.[Li Juan], Lee, H.P.[Heow Pueh], Chong, W.K.[Wai Keong],
Modified support vector novelty detector using training data with outliers,
PRL(24), No. 14, October 2003, pp. 2479-2487.
WWW Link. 0307
BibRef

Foody, G.M., Mathur, A.,
A Relative Evaluation of Multiclass Image Classification by Support Vector Machines,
GeoRS(42), No. 6, June 2004, pp. 1335-1343.
IEEE Abstract. 0407
BibRef

Foody, G.M., Mathur, A.,
Toward intelligent training of supervised image classifications: Directing training data acquisition for SVM classification,
RSE(93), No. 1-2, 2004, pp. 107-117.
Elsevier DOI 1102
BibRef

Zhan, Y.Q.[Yi-Qiang], Shen, D.G.[Ding-Gang],
Design efficient support vector machine for fast classification,
PR(38), No. 1, January 2005, pp. 157-161.
WWW Link. 0410
BibRef

Zhan, Y.Q.[Yi-Qiang], Shen, D.G.[Ding-Gang],
An adaptive error penalization method for training an efficient and generalized SVM,
PR(39), No. 3, March 2006, pp. 342-350.
WWW Link. 0601
BibRef

Lin, C.F.[Chun-Fu], Wang, S.D.[Sheng-De],
Training algorithms for fuzzy support vector machines with noisy data,
PRL(25), No. 14, 15 October 2004, pp. 1647-1656.
WWW Link. 0410
BibRef

Shin, H.J.[Hyun-Jung], Cho, S.Z.[Sung-Zoon],
Invariance of neighborhood relation under input space to feature space mapping,
PRL(26), No. 6, 1 May 2005, pp. 707-718.
WWW Link. 0501
SVM training by determining which will be useful. BibRef

Bazi, Y., Melgani, F.,
Toward an Optimal SVM Classification System for Hyperspectral Remote Sensing Images,
GeoRS(44), No. 11, November 2006, pp. 3374-3385.
IEEE DOI 0611
BibRef

Bazi, Y., Melgani, F.,
Semisupervised PSO-SVM Regression for Biophysical Parameter Estimation,
GeoRS(45), No. 6, June 2007, pp. 1887-1895.
IEEE DOI 0706
See also Gaussian Process Approach to Remote Sensing Image Classification. BibRef

Ghoggali, N., Melgani, F., Bazi, Y.,
A Multiobjective Genetic SVM Approach for Classification Problems With Limited Training Samples,
GeoRS(47), No. 6, June 2009, pp. 1707-1718.
IEEE DOI 0905
BibRef

Ghoggali, N., Melgani, F.,
Automatic Ground-Truth Validation With Genetic Algorithms for Multispectral Image Classification,
GeoRS(47), No. 7, July 2009, pp. 2172-2181.
IEEE DOI 0906
BibRef

Paoli, A., Melgani, F., Pasolli, E.,
Clustering of Hyperspectral Images Based on Multiobjective Particle Swarm Optimization,
GeoRS(47), No. 12, December 2009, pp. 4175-4188.
IEEE DOI 0912
BibRef

Orabona, F.[Francesco], Castellini, C.[Claudio], Caputo, B.[Barbara], Jie, L.[Luo], Sandini, G.[Giulio],
On-line independent support vector machines,
PR(43), No. 4, April 2010, pp. 1402-1412.
Elsevier DOI 1002
BibRef
Earlier:
Indoor Place Recognition using Online Independent Support Vector Machines,
BMVC07(xx-yy).
PDF File. 0709
Support vector machines; On-line learning; Bounded testing complexity; Linear independence BibRef

Jie, L.[Luo], Orabona, F.[Francesco], Fornoni, M.[Marco], Caputo, B.[Barbara], Cesa-Bianchi, N.[Nicolo],
OM-2: An online multi-class Multi-Kernel Learning algorithm,
OLCV10(43-50).
IEEE DOI 1006
BibRef

Orabona, F.[Francesco], Jie, L.[Luo], Caputo, B.[Barbara],
Online-batch strongly convex Multi Kernel Learning,
CVPR10(787-794).
IEEE DOI Video of talk:
WWW Link. 1006
BibRef
Earlier: A2, A1, A3:
An Online Framework for Learning Novel Concepts over Multiple Cues,
ACCV09(I: 269-280).
Springer DOI 0909
BibRef

Tommasi, T.[Tatiana], Orabona, F.[Francesco], Caputo, B.[Barbara],
Learning Categories From Few Examples With Multi Model Knowledge Transfer,
PAMI(36), No. 5, May 2014, pp. 928-941.
IEEE DOI 1405
BibRef
Earlier:
Safety in numbers: Learning categories from few examples with multi model knowledge transfer,
CVPR10(3081-3088).
IEEE DOI 1006
Adaptation models BibRef

Tommasi, T.[Tatiana], Caputo, B.[Barbara],
The more you know, the less you learn: from knowledge transfer to one-shot learning of object categories,
BMVC09(xx-yy).
PDF File. 0909
Learning from only a few examples. BibRef

Kuzborskij, I.[Ilja], Orabona, F.[Francesco], Caputo, B.[Barbara],
Scalable greedy algorithms for transfer learning,
CVIU(156), No. 1, 2017, pp. 174-185.
Elsevier DOI 1702
Transfer learning BibRef

Tommasi, T.[Tatiana], Orabona, F.[Francesco], Kaboli, M.[Mohsen], Caputo, B.[Barbara],
Leveraging over prior knowledge for online learning of visual categories,
BMVC12(87).
DOI Link 1301
BibRef

Jie, L.[Luo], Tommasi, T.[Tatiana], Caputo, B.[Barbara],
Multiclass transfer learning from unconstrained priors,
ICCV11(1863-1870).
IEEE DOI 1201
BibRef

Davenport, M.A.[Mark A.], Baraniuk, R.G.[Richard G.], Scott, C.D.[Clayton D.],
Tuning Support Vector Machines for Minimax and Neyman-Pearson Classification,
PAMI(32), No. 10, October 2010, pp. 1888-1898.
IEEE DOI 1008
SVM training. Neyman-Pearson usually is sensitive to errors. BibRef

Bellala, G.[Gowtham], Stanley, J.[Jason], Bhavnani, S.K.[Suresh K.], Scott, C.D.[Clayton D.],
A Rank-Based Approach to Active Diagnosis,
PAMI(35), No. 9, 2013, pp. 2078-2090.
IEEE DOI 1307
Approximation methods. Disease diagnosis or network fault analysis. BibRef

Kim, J.S.[Joo-Seuk], Scott, C.D.[Clayton D.],
L(2) Kernel Classification,
PAMI(32), No. 10, October 2010, pp. 1822-1831.
IEEE DOI 1008
Vs. KDE and SVM approaches. Optimizes the integrated squared error (ISE) of a difference of densities. L_2 usually poor for higher dimensions (vs. SVM), regularization to improve this. BibRef

Maulik, U.[Ujjwal], Chakraborty, D.[Debasis],
A self-trained ensemble with semisupervised SVM: An application to pixel classification of remote sensing imagery,
PR(44), No. 3, March 2011, pp. 615-623.
Elsevier DOI 1011
Semisupervised learning; Support vector machines; Remote sensing satellite images; Quadratic programming; Self-training; Classifier ensemble BibRef

Maulik, U.[Ujjwal], Chakraborty, D.[Debasis],
Learning with transductive SVM for semisupervised pixel classification of remote sensing imagery,
PandRS(77), No. 1, March 2013, pp. 66-78.
Elsevier DOI 1303
Pixel classification; Support vector machines; Remote sensing satellite images; Quadratic programming; Semisupervised classification; Transductive learning BibRef

Airola, A.[Antti], Pahikkala, T.[Tapio], Salakoski, T.[Tapio],
Training linear ranking SVMs in linearithmic time using red-black trees,
PRL(32), No. 9, 1 July 2011, pp. 1328-1336.
Elsevier DOI 1101
Bundle methods; Cutting plane method; Learning to rank; Ranking support vector machine; Red-black tree BibRef

Heimonen, J.H.[Ju-Ho], Salakoski, T.[Tapio], Pahikkala, T.[Tapio],
Properties of Object-Level Cross-Validation Schemes for Symmetric Pair-Input Data,
SSSPR14(384-393).
Springer DOI 1408
BibRef

Rivas-Perea, P.[Pablo], Cota-Ruiz, J.[Juan],
An algorithm for training a large scale support vector machine for regression based on linear programming and decomposition methods,
PRL(34), No. 4, 1 March 2013, pp. 439-451.
Elsevier DOI 1302
BibRef
And: Corrigendum: See also Corrigendum to An algorithm for training a large scale support vector machine for regression based on linear programming and decomposition methods. Support vector machines; Support Vector Regression; Linear programming; Interior point methods BibRef

Rivas-Perea, P.[Pablo], Cota-Ruiz, J.[Juan], Rosiles, J.G.[Jose-Gerardo],
Corrigendum to 'An algorithm for training a large scale support vector machine for regression based on linear programming and decomposition methods',
PRL(34), No. 6, 15 April 2013, pp. 678.
Elsevier DOI 1303
See also algorithm for training a large scale support vector machine for regression based on linear programming and decomposition methods, An. BibRef

Prato, M., Zanni, L.,
A practical use of regularization for supervised learning with kernel methods,
PRL(34), No. 6, 15 April 2013, pp. 610-618.
Elsevier DOI 1303
Regularization algorithms; Kernel methods; Support vector machines; Conjugate gradient; Inverse problems BibRef

Xu, S.[Shuo], An, X.[Xin], Qiao, X.D.[Xiao-Dong], Zhu, L.J.[Li-Jun], Li, L.[Lin],
Multi-output least-squares support vector regression machines,
PRL(34), No. 9, July 2013, pp. 1078-1084.
Elsevier DOI 1305
Least-squares support vector regression machine (LS-SVR); Multiple task learning (MTL); Multi-output LS-SVR (MLS-SVR); Positive definite matrix BibRef

Mordelet, F., Vert, J.P.,
A bagging SVM to learn from positive and unlabeled examples,
PRL(37), No. 1, 2014, pp. 201-209.
Elsevier DOI 1402
PU learning BibRef

Anguita, D.[Davide], Ghio, A.[Alessandro], Oneto, L.[Luca], Ridella, S.[Sandro],
Unlabeled patterns to tighten Rademacher complexity error bounds for kernel classifiers,
PRL(37), No. 1, 2014, pp. 210-219.
Elsevier DOI 1402
Support vector machine BibRef

Singla, A.[Anshu], Patra, S.[Swarnajyoti], Bruzzone, L.[Lorenzo],
A novel classification technique based on progressive transductive SVM learning,
PRL(42), No. 1, 2014, pp. 101-106.
Elsevier DOI 1404
Cluster assumption BibRef

Jung, H.G., Kim, G.,
Support Vector Number Reduction: Survey and Experimental Evaluations,
ITS(15), No. 2, April 2014, pp. 463-476.
IEEE DOI 1404
Approximation error BibRef

Doan, T.N.[Thanh-Nghi], Do, T.N.[Thanh-Nghi], Poulet, F.[François],
Parallel incremental power mean SVM for the classification of large-scale image datasets,
MultInfoRetr(3), No. 2, June 2014, pp. 89-96.
Springer DOI 1407
BibRef

Guo, H.S.[Hu-Sheng], Wang, W.J.[Wen-Jian],
An active learning-based SVM multi-class classification model,
PR(48), No. 5, 2015, pp. 1577-1597.
Elsevier DOI 1502
Multi-class classification with unknown categories BibRef

Carrasco, M.[Miguel], López, J.[Julio], Maldonado, S.[Sebastián],
A multi-class SVM approach based on the L1-norm minimization of the distances between the reduced convex hulls,
PR(48), No. 5, 2015, pp. 1598-1607.
Elsevier DOI 1502
Multi-class classification BibRef

Zhang, Y.Q.[Yun-Qiang], Zhang, P.L.[Pei-Lin],
Machine training and parameter settings with social emotional optimization algorithm for support vector machine,
PRL(54), No. 1, 2015, pp. 36-42.
Elsevier DOI 1502
Support vector machine BibRef

Chang, C.C.[Chin-Chun], Chou, S.H.[Shen-Huan],
Tuning of the hyperparameters for L2-loss SVMs with the RBF kernel by the maximum-margin principle and the jackknife technique,
PR(48), No. 12, 2015, pp. 3983-3992.
Elsevier DOI 1509
RBF kernels BibRef

Iosifidis, A.[Alexandros], Gabbouj, M.[Moncef],
On the kernel Extreme Learning Machine speedup,
PRL(68, Part 1), No. 1, 2015, pp. 205-210.
Elsevier DOI 1512
Kernel extreme learning machine BibRef

Iosifidis, A.[Alexandros], Gabbouj, M.[Moncef],
Multi-class Support Vector Machine classifiers using intrinsic and penalty graphs,
PR(55), No. 1, 2016, pp. 231-246.
Elsevier DOI 1604
Multi-class classification BibRef

Iosifidis, A.[Alexandros], Gabbouj, M.[Moncef],
Nyström-based approximate kernel subspace learning,
PR(57), No. 1, 2016, pp. 190-197.
Elsevier DOI 1605
Nonlinear pattern recognition BibRef

Iosifidis, A.[Alexandros], Gabbouj, M.[Moncef],
Prototype-based class-specific nonlinear subspace learning for large-scale face verification,
IPTA16(1-6)
IEEE DOI 1703
Face Recognition. face recognition BibRef

Iosifidis, A.[Alexandros], Gabbouj, M.[Moncef],
Hierarchical class-specific kernel discriminant analysis for face verification,
VCIP16(1-4)
IEEE DOI 1701
Face BibRef

Vural, E., Guillemot, C.,
Out-of-Sample Generalizations for Supervised Manifold Learning for Classification,
IP(25), No. 3, March 2016, pp. 1410-1424.
IEEE DOI 1602
Interpolation BibRef

Loosli, G.[Gaëlle], Canu, S.[Stéphane], Ong, C.S.[Cheng Soon],
Learning SVM in Krein Spaces,
PAMI(38), No. 6, June 2016, pp. 1204-1216.
IEEE DOI 1605
Cost function BibRef

Canu, S.[Stéphane],
Recent Advances in Kernel Machines,
CIARP10(1).
Springer DOI 1011
SVM techniques. BibRef

Montagner, I.S., Hirata, N.S.T., Hirata, R., Canu, S.[Stéphane],
NILC: A two level learning algorithm with operator selection,
ICIP16(1873-1877)
IEEE DOI 1610
Computational efficiency BibRef

Moraes, D.[Daniel], Wainer, J.[Jacques], Rocha, A.[Anderson],
Low false positive learning with support vector machines,
JVCIR(38), No. 1, 2016, pp. 340-350.
Elsevier DOI 1605
Support vector machines BibRef

Zhang, Y.S.[Yong-Shan], Wu, J.[Jia], Cai, Z.H.[Zhi-Hua], Zhang, P.[Peng], Chen, L.[Ling],
Memetic Extreme Learning Machine,
PR(58), No. 1, 2016, pp. 135-148.
Elsevier DOI 1606
Extreme Learning Machine BibRef

Houthooft, R.[Rein], De Turck, F.[Filip],
Integrated inference and learning of neural factors in structural support vector machines,
PR(59), No. 1, 2016, pp. 292-301.
Elsevier DOI 1705
Structural support vector machine BibRef

Zhu, F.[Fa], Yang, J.[Jian], Gao, J.B.[Jun-Bin], Xu, C.Y.[Chun-Yan],
Extended nearest neighbor chain induced instance-weights for SVMs,
PR(60), No. 1, 2016, pp. 863-874.
Elsevier DOI 1609
Weighted support vector machine BibRef

Zhang, G.Q.[Guo-Qing], Sun, H.J.[Huai-Jiang], Ji, Z.X.[Ze-Xuan], Xia, G.Y.[Gui-Yu], Feng, L.[Lei], Sun, Q.S.[Quan-Sen],
Kernel dictionary learning based discriminant analysis,
JVCIR(40, Part B), No. 1, 2016, pp. 470-484.
Elsevier DOI 1610
Kernel method BibRef

Fan, Q.[Qi], Wang, Z.[Zhe], Zha, H.Y.[Hong-Yuan], Gao, D.[Daqi],
MREKLM: A fast multiple empirical kernel learning machine,
PR(61), No. 1, 2017, pp. 197-209.
Elsevier DOI 1705
Multiple Kernel Learning BibRef

Dong, A., Chung, F.l., Deng, Z., Wang, S.,
Semi-Supervised SVM With Extended Hidden Features,
Cyber(46), No. 12, December 2016, pp. 2924-2937.
IEEE DOI 1612
Convergence BibRef

Jung, H.G.[Ho Gi],
Support vector number reduction by extending iterative preimage addition using genetic algorithm-based preimage estimation,
PRL(84), No. 1, 2016, pp. 43-48.
Elsevier DOI 1612
Support vector number reduction (SVNR) BibRef

Morales, R.D.[Roberto Díaz], Vázquez, Á.N.[Ángel Navia],
Improving the efficiency of IRWLS SVMs using parallel Cholesky factorization,
PRL(84), No. 1, 2016, pp. 91-98.
Elsevier DOI 1612
Support Vector Machines BibRef

Duarte, E.[Edson], Wainer, J.[Jacques],
Empirical comparison of cross-validation and internal metrics for tuning SVM hyperparameters,
PRL(88), No. 1, 2017, pp. 6-11.
Elsevier DOI 1703
SVM BibRef

Chu, D.[Dejun], Zhang, C.S.[Chang-Shui], Tao, Q.[Qing],
A faster cutting plane algorithm with accelerated line search for linear SVM,
PR(67), No. 1, 2017, pp. 127-138.
Elsevier DOI 1704
Linear support vector machine BibRef

Ding, S.[Shifei], Zhang, X.[Xiekai], An, Y.[Yuexuan], Xue, Y.[Yu],
Weighted linear loss multiple birth support vector machine based on information granulation for multi-class classification,
PR(67), No. 1, 2017, pp. 32-46.
Elsevier DOI 1704
Multi-class classification BibRef

Du, M.J.[Ming-Jing], Ding, S.[Shifei], Xue, Y.[Yu],
A novel density peaks clustering algorithm for mixed data,
PRL(97), No. 1, 2017, pp. 46-53.
Elsevier DOI 1709
Data clustering BibRef

Ping, Y.[Yuan], Tian, Y.J.[Ying-Jie], Guo, C.[Chun], Wang, B.C.[Bao-Cang], Yang, Y.[Yuehua],
FRSVC: Towards making support vector clustering consume less,
PR(69), No. 1, 2017, pp. 286-298.
Elsevier DOI 1706
Large-scale, data BibRef

Tharwat, A.[Alaa], Hassanien, A.E.[Aboul Ella], Elnaghi, B.E.[Basem E.],
A BA-based algorithm for parameter optimization of Support Vector Machine,
PRL(93), No. 1, 2017, pp. 13-22.
Elsevier DOI 1706
Optimization, algorithms BibRef

Wang, X.[Xin], Thome, N.[Nicolas], Cord, M.[Matthieu],
Gaze latent support vector machine for image classification improved by weakly supervised region selection,
PR(72), No. 1, 2017, pp. 59-71.
Elsevier DOI 1708
BibRef
Earlier:
Gaze latent support vector machine for image classification,
ICIP16(236-240)
IEEE DOI 1610
Weakly supervised learning. Computational modeling BibRef

González-Serrano, F.J.[Francisco-Javier], Navia-Vázquez, Á.[Ángel], Amor-Martín, A.[Adrián],
Training Support Vector Machines with privacy-protected data,
PR(72), No. 1, 2017, pp. 93-107.
Elsevier DOI 1708
Machine learning BibRef

Zuo, W., Wang, F., Zhang, D., Lin, L., Huang, Y., Meng, D., Zhang, L.,
Distance Metric Learning via Iterated Support Vector Machines,
IP(26), No. 10, October 2017, pp. 4937-4950.
IEEE DOI 1708
Face, Kernel, Learning systems, Measurement, Optimization, Support vector machines, Training, Lagrange duality, Metric learning, alternating minimization, kernel method, BibRef

Chum, O.[Ondrej],
Optimizing explicit feature maps on intervals,
IVC(66), No. 1, 2017, pp. 36-47.
Elsevier DOI 1710
Explicit feature maps BibRef


Kawulok, M.[Michal], Nalepa, J.[Jakub], Dudzik, W.[Wojciech],
An Alternating Genetic Algorithm for Selecting SVM Model and Training Set,
MCPR17(94-104).
Springer DOI 1706
BibRef

Guo, X.F.[Xi-Feng], Chen, W.[Wei], Yin, J.P.[Jian-Ping],
A simple approach for unsupervised domain adaptation,
ICPR16(1566-1570)
IEEE DOI 1705
Benchmark testing, Kernel, Manifolds, Standards, Support vector machines, Training BibRef

Saikia, G., Shivagunde, S., Saradhi, V.V., Kannao, R.D., Guha, P.,
Multiple kernel learning using data envelopment analysis and feature vector selection and projection,
ICPR16(520-524)
IEEE DOI 1705
Complexity theory, Data envelopment analysis, Data models, Kernel, Mathematical model, Support vector machines BibRef

Langenkämper, D., Nattkemper, T.W.,
COATL: A learning architecture for online real-time detection and classification assistance for environmental data,
ICPR16(597-602)
IEEE DOI 1705
Computer architecture, Feature extraction, Image color analysis, Labeling, Real-time systems, Support vector machines, Training BibRef

Mao, X.[Xue], Fu, Z.Y.[Zhou-Yu], Wu, O.[Ou], Hu, W.M.[Wei-Ming],
Fast kernel SVM training via support vector identification,
ICPR16(1554-1559)
IEEE DOI 1705
Kernel, Mathematical model, Prediction algorithms, Standards, Support vector machines, Training, Training, data BibRef

Liu, Y.W.[Yang-Wei], Xu, J.,
One-pass online SVM with extremely small space complexity,
ICPR16(3482-3487)
IEEE DOI 1705
Algorithm design and analysis, Approximation algorithms, Computer science, Pattern recognition, Support vector machines, Training, Training, data BibRef

Li, W., Dai, D., Tan, M., Xu, D., Van Gool, L.J.,
Fast Algorithms for Linear and Kernel SVM+,
CVPR16(2258-2266)
IEEE DOI 1612
BibRef

Mohapatra, P.[Pritish], Dokania, P.K.[Puneet Kumar], Jawahar, C.V., Kumar, M.P.[M. Pawan],
Partial Linearization Based Optimization for Multi-class SVM,
ECCV16(V: 842-857).
Springer DOI 1611
BibRef

Namin, S.R., Alvarez, J.M., Kneip, L., Petersson, L.,
Latent structural SVM with marginal probabilities for weakly labeled structured learning,
ICIP16(3733-3737)
IEEE DOI 1610
Algorithm design and analysis BibRef

Hoseinkhani, F., Nasersharif, B.,
A feature transformation method based on multi objective particle swarm optimization for reducing support vector machine error,
IPRIA15(1-6)
IEEE DOI 1603
feature extraction BibRef

Bouchacourt, D.[Diane], Nowozin, S.[Sebastian], Kumar, M.P.[M. Pawan],
Entropy-Based Latent Structured Output Prediction,
ICCV15(2920-2928)
IEEE DOI 1602
Computer vision BibRef

Goto, M.[Masanori], Ishida, R.[Ryosuke], Uchida, S.[Seiichi],
Preselection of support vector candidates by relative neighborhood graph for large-scale character recognition,
ICDAR15(306-310)
IEEE DOI 1511
BibRef

Stamos, D.[Dimitris], Martelli, S.[Samuele], Nabi, M.[Moin], McDonald, A.[Andrew], Murino, V.[Vittorio], Pontil, M.[Massimiliano],
Learning with dataset bias in latent subcategory models,
CVPR15(3650-3658)
IEEE DOI 1510
Latent subcategory models for training SVM BibRef

Shah, N.[Neel], Kolmogorov, V.[Vladimir], Lampert, C.H.[Christoph H.],
A multi-plane block-coordinate frank-wolfe algorithm for training structural SVMs with a costly max-oracle,
CVPR15(2737-2745)
IEEE DOI 1510
BibRef

Xu, X.[Xing], Shimada, A.[Atsushi], Taniguch, R.I.[Rin-Ichiro],
Exploring Image Specific Structured Loss for Image Annotation with Incomplete Labelling,
ACCV14(I: 704-719).
Springer DOI 1504
BibRef

Durand, T.[Thibaut], Thome, N.[Nicolas], Cord, M.[Matthieu],
MANTRA: Minimum Maximum Latent Structural SVM for Image Classification and Ranking,
ICCV15(2713-2721)
IEEE DOI 1602
Detectors BibRef

Durand, T.[Thibaut], Mordan, T., Thome, N.[Nicolas], Cord, M.[Matthieu],
WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation,
CVPR17(5957-5966)
IEEE DOI 1711
BibRef
Earlier: A1, A3, A4, Only:
WELDON: Weakly Supervised Learning of Deep Convolutional Neural Networks,
CVPR16(4743-4752)
IEEE DOI 1612
Dogs, Feature extraction, Head, Image segmentation, Legged locomotion, Object detection, Semantics BibRef

Durand, T.[Thibaut], Thome, N.[Nicolas], Cord, M.[Matthieu], Picard, D.[David],
Incremental learning of latent structural SVM for weakly supervised image classification,
ICIP14(4246-4250)
IEEE DOI 1502
Agriculture BibRef

Blondel, M.[Mathieu], Fujino, A.[Akinori], Ueda, N.[Naonori],
Large-Scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex,
ICPR14(1289-1294)
IEEE DOI 1412
Accuracy BibRef

Chen, Y.D.[Yao-Dong], Li, R.[Renfa],
Effective Part Localization in Latent-SVM Training,
ICPR14(4269-4274)
IEEE DOI 1412
Detectors BibRef

Álvarez-Meza, A.M.[Andrés Marino], Cárdenas-Peña, D., Castellanos-Domínguez, C.G.[César Germán],
Unsupervised Kernel Function Building Using Maximization of Information Potential Variability,
CIARP14(335-342).
Springer DOI 1411
BibRef

Bilen, H.[Hakan], Pedersoli, M.[Marco], Namboodiri, V.P.[Vinay P.], Tuytelaars, T.[Tinne], Van Gool, L.J.[Luc J.],
Object Classification with Adaptable Regions,
CVPR14(3662-3669)
IEEE DOI 1409
latent svm; object classification; weakly supervised detection BibRef

Behl, A.[Aseem], Mohapatra, P., Jawahar, C.V., Kumar, M.P.[M. Pawan],
Optimizing Average Precision Using Weakly Supervised Data,
PAMI(37), No. 12, December 2015, pp. 2545-2557.
IEEE DOI 1512
BibRef
Earlier: A1, A3, A4, Only: CVPR14(1011-1018)
IEEE DOI 1409
character recognition Optimization methods; Statistical methods and learning BibRef

Yang, J.[Jiyan], Sindhwani, V.[Vikas], Fan, Q.F.[Quan-Fu], Avron, H.[Haim], Mahoney, M.[Michael],
Random Laplace Feature Maps for Semigroup Kernels on Histograms,
CVPR14(971-978)
IEEE DOI 1409
BibRef

Takami, M.[Masato], Bell, P.[Peter], Ommer, B.[Bjorn],
Offline learning of prototypical negatives for efficient online Exemplar SVM,
WACV14(377-384)
IEEE DOI 1406
Art BibRef

Chen, D.Z.[Dao-Zheng], Batra, D.[Dhruv], Freeman, W.T.[William T.],
Group Norm for Learning Structured SVMs with Unstructured Latent Variables,
ICCV13(409-416)
IEEE DOI 1403
Concave-Convex Procedure BibRef

Vemulapalli, R.[Raviteja], Pillai, J.K.[Jaishanker K.], Chellappa, R.[Rama],
Kernel Learning for Extrinsic Classification of Manifold Features,
CVPR13(1782-1789)
IEEE DOI 1309
Extrinsic Classification. Features in non-linear space harder to cluster. BibRef

Branson, S.[Steve], Beijbom, O.[Oscar], Belongie, S.J.[Serge J.],
Efficient Large-Scale Structured Learning,
CVPR13(1806-1813)
IEEE DOI 1309
cost-sensitive SVM BibRef

Candel, D.[Diego], Ñanculef, R.[Ricardo], Concha, C.[Carlos], Allende, H.[Héctor],
A Sequential Minimal Optimization Algorithm for the All-Distances Support Vector Machine,
CIARP10(484-491).
Springer DOI 1011
BibRef

Frandi, E.[Emanuele], Gasparo, M.G.[Maria Grazia], Lodi, S.[Stefano], Ñanculef, R.[Ricardo], Sartori, C.[Claudio],
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CIARP10(87-95).
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Zhang, D.Y.[De-Yuan], Wang, X.L.[Xiao-Long], Liu, B.Q.[Bing-Quan],
Learning the Kernel Combination for Object Categorization,
ICPR10(2929-2932).
IEEE DOI 1008
Learn optimal combination of kernels before SVM training BibRef

Timm, F.[Fabian], Klement, S.[Sascha], Martinetz, T.[Thomas],
Fast model selection for MaxMinOver-based training of support vector machines,
ICPR08(1-4).
IEEE DOI 0812
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Sentelle, C.[Christopher], Anagnostopoulos, G.C.[Georgios C.], Georgiopoulos, M.[Michael],
A fast revised simplex method for SVM training,
ICPR08(1-4).
IEEE DOI 0812
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Schnitzspan, P.[Paul], Fritz, M.[Mario], Schiele, B.[Bernt],
Hierarchical Support Vector Random Fields: Joint Training to Combine Local and Global Features,
ECCV08(II: 527-540).
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Mejía-Guevara, I.[Iván], Kuri-Morales, Á.[Ángel],
MP-Polynomial Kernel for Training Support Vector Machines,
CIARP07(584-593).
Springer DOI 0711
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Lebrun, G., Charrier, C., Cardot, H.,
SVM training time reduction using vector quantization,
ICPR04(I: 160-163).
IEEE DOI 0409
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Yang, M.H.[Ming-Hsuan], Ahuja, N.[Narendra],
A Geometric Approach to Train Support Vector Machines,
CVPR00(I: 430-437).
IEEE DOI 0005
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Support Vector Machines, SVM, Incremental, Multi-Step .


Last update:Nov 18, 2017 at 20:56:18