14.2.16 Support Vector Machines, SVM

Pontil, M.[Massimiliano], Verri, A.[Alessandro],
Support Vector Machines for 3D Object Recognition,
PAMI(20), No. 6, June 1998, pp. 637-646.
IEEE Abstract. IEEE Top Reference.
WWW Version. 9807
BibRef
Earlier:
Direct aspect-based 3-D object recognition,
CIAP97(II: 300-307).
WWW Version. 9709
Given a set of points a linear SVM finds the hyperplane that best divides the set (maximum distance from the plane, maximize correct classification). Support vectors are subsets of points in the classes. Apply to the same kinds of problems as appearance based matching. BibRef

Pontil, M., Rogai, S., Verri, A.,
Recognizing 3-D objects with linear support vector machines,
ECCV98(II: 469).
WWW Version. BibRef 9800

Pittore, M., Basso, C., Verri, A.,
Representing and recognizing visual dynamic events with support vector machines,
CIAP99(18-23).
IEEE DOI Link 9909
BibRef

Barzilay, O.[Ofir], Brailovsky, V.L.,
On domain knowledge and feature selection using a support vector machine,
PRL(20), No. 5, May 1999, pp. 475-484. BibRef 9905

Wolf, L.[Lior], Shashua, A.[Amnon],
Learning over sets using kernel principal angles,
MachLearnRes(4), 2003, pp. 913-931.
WWW Version. BibRef 0300
Earlier:
Feature selection for unsupervised and supervised inference: The emergence of sparsity in a weighted-based approach,
ICCV03(378-384).
IEEE DOI Link 0311
BibRef
And:
Kernel principal angles for classification machines with applications to image sequence interpretation,
CVPR03(I: 635-640).
IEEE Abstract. IEEE Top Reference. 0307
BibRef

Wolf, L., Shashua, A., Mukherjee, S.,
Gene Selection via a Spectral Approach,
BioInfo05(III: 140-140).
IEEE DOI Link 0507
BibRef

Shashua, A.[Amnon], Wolf, L.[Lior],
Kernel Feature Selection with Side Data Using a Spectral Approach,
ECCV04(Vol III: 39-53).
WWW Version. 0405
BibRef

Vishwanathan, S.V.N., Smola, A.J.[Alexander J.], Vidal, R.[René],
Binet-Cauchy Kernels on Dynamical Systems and its Application to the Analysis of Dynamic Scenes,
IJCV(73), No. 1, June 2007, pp. 95-119.
Springer DOI Link 0702
Unify all kernel learning approaches. BibRef

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 Link 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 Version. 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 Abstract. IEEE Top Reference.
WWW Version. 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 Link 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.
HTML Version. 0108
BibRef

Guillamet, D.[David], Vitrià, J.[Jordi],
Discriminant Local Regions Using Support Vector Machines,
ELCVIA(2002), No. 0 2002, pp. None.
WWW Version. 0206
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 Version. 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. IEEE Top Reference. 0403
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.
HTML Version. 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

Song, Q.[Qing], Hu, W.J.[Wen-Jie], Xie, W.F.[Wen-Fang],
Robust support vector machine with bullet hole image classification,
SMC-C(32), No. 4, November 2002, pp. 440-448.
IEEE Top Reference. 0301
BibRef

Parrado-Hernández, E., 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 Version. 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 Link 0905
based on the Kullback-Leibler divergence. BibRef

Lau, K.W., Wu, Q.H.,
Online training of support vector classifier,
PR(36), No. 8, August 2003, pp. 1913-1920.
WWW Version. 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 Version. 0407
BibRef

Fei, Y.N., Lu, Z., Tang, W.H., Wu, Q.H.,
Harmonic Estimation Using a Global Search Optimiser,
EvoIASP07(261-270).
Springer DOI Link 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 Version. 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 Version. 0304
BibRef

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 Version. 0307
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. IEEE Top Reference. 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. IEEE Top Reference. 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 Version. 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 Version. 0311
BibRef

Kumar, R., Kulkarni, A., Jayaraman, V.K., Kulkarni, B.D.,
Symbolization assisted SVM classifier for noisy data,
PRL(25), No. 4, March 2004, pp. 495-504.
WWW Version. 0402
BibRef

Kumar, R., Jayaraman, V.K., Kulkarni, B.D.,
An SVM classifier incorporating simultaneous noise reduction and feature selection: illustrative case examples,
PR(38), No. 1, January 2005, pp. 41-49.
WWW Version. 0410
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. IEEE Top Reference. 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. IEEE Top Reference. 0404
BibRef
Earlier:
Speeding up SVM decision based on mirror points,
ICPR02(II: 869-872).
IEEE DOI Link 0211
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. IEEE Top Reference. 0407
BibRef

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

Zhan, Y.Q.A.[Yi-Qi-Ang], 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 Version. 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 Version. 0410
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. IEEE Top Reference. 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 Link 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 Version. 0611
Multi-class classification; Kernel methods; Bayes decision theory; Density estimation; Support vector domain description BibRef

Mantero, P., Moser, G., Serpico, S.B.,
Partially Supervised Classification of Remote Sensing Images Through SVM-Based Probability Density Estimation,
GeoRS(43), No. 3, March 2005, pp. 559-570.
IEEE Abstract. IEEE Top Reference. 0501
See also Conditional Copulas for Change Detection in Heterogeneous Remote Sensing Images. BibRef

Haasdonk, B.[Bernard],
Feature Space Interpretation of SVMs with Indefinite Kernels,
PAMI(27), No. 4, April 2005, pp. 482-492.
IEEE Abstract. IEEE Top Reference. 0501
BibRef

Haasdonk, B., Keysers, D.,
Tangent distance kernels for support vector machines,
ICPR02(II: 864-868).
IEEE DOI Link 0211
BibRef

Haasdonk, B.[Bernard], Bahlmann, C.[Claus],
Learning with Distance Substitution Kernels,
DAGM04(220-227).
WWW Version. 0505
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 Version. 0501
SVM training by determining which will be useful. 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 Version. 0505
BibRef

Tran, Q.A.[Quang-Anh], Li, X.[Xing], Duan, H.X.[Hai-Xin],
Efficient performance estimate for one-class support vector machine,
PRL(26), No. 8, June 2005, pp. 1174-1182.
WWW Version. 0506
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 Version. 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 Version. 0611
Model selection; SVM; Kernel; Hyperparameters; Optimizing time 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 Version. 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 Version. 0508
BibRef

González, L., Angulo, C., Velasco, F., Català, A.,
Dual unification of bi-class support vector machine formulations,
PR(39), No. 7, July 2006, pp. 1325-1332.
WWW Version. 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 Version. 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 Version. 0508
See also Rough set based incremental clustering of interval data. BibRef

Asharaf, S., Murty, M.N.[M. Narasimha],
Scalable non-linear Support Vector Machine using hierarchical clustering,
ICPR06(I: 908-911).
WWW Version. 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 Version. Clustering; Support vector machines; R*-tree 0606
BibRef

Kikuchi, T.[Tomonori], Abe, S.[Shigeo],
Comparison between error correcting output codes and fuzzy support vector machines,
PRL(26), No. 12, September 2005, pp. 1937-1945.
WWW Version. 0508
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 Version. 0509
BibRef

Muñoz, A.[Alberto], Moguerza, J.M.[Javier M.],
Estimation of High-Density Regions Using One-Class Neighbor Machines,
PAMI(28), No. 3, March 2006, pp. 476-480.
IEEE DOI Link 0602
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 Version. 0605
Early stopping; Robustness; SVM See also Algorithm for Linear Inequalities and its Applications, An. BibRef

Pozdnoukhov, A.[Alexei], Bengio, S.[Samy],
Invariances in kernel methods: From samples to objects,
PRL(27), No. 10, 15 July 2006, pp. 1087-1097.
WWW Version. 0606
BibRef
And:
Graph-based transformation manifolds for invariant pattern recognition with kernel methods,
ICPR06(III: 1228-1231).
WWW Version. 0609
BibRef
And: ICPR06(IV: 956).
WWW Version. 0609
BibRef
Earlier:
Tangent vector kernels for invariant image classification with SVMs,
ICPR04(III: 486-489).
IEEE DOI Link 0409
Kernel methods; SVM; Invariances; Tangent vectors BibRef

Mariethoz, J.[Johnny], Bengio, S.[Samy],
A kernel trick for sequences applied to text-independent speaker verification systems,
PR(40), No. 8, August 2007, pp. 2315-2324.
WWW Version. 0704
Support vector machines; Gaussian mixture models; Sequence kernel; Text-independent speaker verification 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 Link 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 Version. 0606
BibRef
Earlier:
SVM-based classifier design with controlled confidence,
ICPR04(I: 164-167).
IEEE DOI Link 0409
Confidence-based classification; Error estimation; Reject option; Dynamic bin width allocation 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 Version. Incremental training; Hyperspheres; 0606
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 Version. 0608
Multi-class classifier; SVMlight approach; Objective function 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 Version. 0611
Incremental training; Active learning; Support vector machine; Clustering algorithm; Pattern classification See also Improved feature reduction in input and feature spaces. 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 Version. 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 Link 0701
BibRef

Chen, Y.X.[Yi-Xin], Bi, J.B.[Jin-Bo], Wang, J.Z.[James Z.],
MILES: Multiple-Instance Learning via Embedded Instance Selection,
PAMI(28), No. 12, December 2006, pp. 1931-1947.
IEEE DOI Link 0611
BibRef
Earlier: A2, A1, A3:
A Sparse Support Vector Machine Approach to Region-Based Image Categorization,
CVPR05(I: 1121-1128).
IEEE DOI Link 0507
Training labels on sets of instances not single instances. 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 Link 0704
A binary SVM classifier that determines two nonparallel planes by solving two related SVM-type problems. 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 Version. 0705
Support vector machines; Decomposition methods; Convergence; Statistical learning theory; Pattern recognition 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 Link 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 Link 0706
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 Link 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 Link 0906
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 Link 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 Link 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 Version. 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 Version. 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 Link 0710
BibRef

Wang, J.S.[Jeen-Shing], Chiang, J.C.[Jen-Chieh],
A cluster validity measure with a hybrid parameter search method for the support vector clustering algorithm,
PR(41), No. 2, February 2008, pp. 506-520.
WWW Version. 0711
Support vector clustering; Cluster validity measure; Parameter learning; Parameter selection BibRef

Wang, J.S.[Jeen-Shing], Chiang, J.C.[Jen-Chieh],
A Cluster Validity Measure With Outlier Detection for Support Vector Clustering,
SMC-B(38), No. 1, February 2007, pp. 78-89.
IEEE DOI Link 0801
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 Version. 0711
SVM; Multi-category classification; Pairwise; Uncertainty sampling BibRef

Zafeiriou, S., Tefas, A., Pitas, I.,
Minimum Class Variance Support Vector Machines,
IP(16), No. 10, October 2007, pp. 2551-2564.
IEEE DOI Link 0711
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 Version. 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 Link 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 Version. 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 Version. 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 Version. 0711
Relevance vector machine; Unbalanced data; Noise; Fuzzy membership; Bayesian inference BibRef

Wang, L.[Lei],
Feature Selection with Kernel Class Separability,
PAMI(30), No. 9, September 2008, pp. 1534-1546.
IEEE DOI Link 0808
BibRef
Earlier:
Feature Subset Selection for Multi-class SVM Based Image Classification,
ACCV07(II: 145-154).
Springer DOI Link 0711
See also Texture classification using multiresolution Markov random field models. BibRef

Wang, L.[Lei], Chan, K.L.[Kap Luk], Tan, Y.P.[Yap Peng],
Image retrieval with SVM active learning embedding Euclidean search,
ICIP03(I: 725-728).
IEEE Abstract. IEEE Top Reference. 0312
BibRef

Wang, L.[Lei], Xue, P.[Ping], Chan, K.L.[Kap Luk],
Incorporating prior knowledge into SVM for image retrieval,
ICPR04(II: 981-984).
IEEE DOI Link 0409
BibRef

Li, X.C.[Xa-Chan], Wang, L.[Lei], Sang, E.[Eric],
Multi-label SVM active learning for image classification,
ICIP04(IV: 2207-2210).
IEEE DOI Link 0505
BibRef

Wang, L.[Lei], Chan, K.L.[Kap Luk], Zhang, Z.H.[Zhi-Hua],
Bootstrapping SVM active learning by incorporating unlabelled images for image retrieval,
CVPR03(I: 629-634).
IEEE Abstract. IEEE Top Reference. 0307
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 Version. 0804
Support vector machines; Pattern recognition; Twin support vector machines 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 Version. 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 Version. 0809
Outlier detection; Support vector domain description BibRef

El-Yaniv, R.[Ran], Pechyony, D.[Dmitry], Yom-Tov, E.[Elad],
Better multiclass classification via a margin-optimized single binary problem,
PRL(29), No. 14, October 2008, pp. 1954-1959.
WWW Version. 0804
Multiclass classification; Support vector machines; Multiple kernel learning 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 Link 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 Link
WWW Version. 0902
SVM; Support vector machines; Kernel parameters; Inter-cluster distances BibRef

Tang, Y., Zhang, Y.Q., Chawla, N.V., Krasser, S.,
SVMs Modeling for Highly Imbalanced Classification,
SMC-B(39), No. 1, February 2009, pp. 281-288.
IEEE DOI Link 0902
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 Link
WWW Version. 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 Link 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 Link
WWW Version. 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 Link 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 Link
WWW Version. 0906
BibRef
Earlier:
Auto-Correlation Wavelet Support Vector Machine and Its Applications to Regression,
CRV05(246-252).
IEEE DOI Link 0505
Wavelets; Support vector machine; Machine learning; Pattern recognition; Function regression; Auto-correlation BibRef

Bruzzone, L., Persello, C.,
A Novel Context-Sensitive Semisupervised SVM Classifier Robust to Mislabeled Training Samples,
GeoRS(47), No. 7, July 2009, pp. 2142-2154.
IEEE DOI Link 0906
BibRef

Bruzzone, L.[Lorenzo], Persello, C.,
A Novel Approach to the Selection of Spatially Invariant Features for the Classification of Hyperspectral Images With Improved Generalization Capability,
GeoRS(47), No. 9, September 2009, pp. 3180-3191.
IEEE DOI Link 0909
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 Link 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 Link
WWW Version. 0907
Sparsity; Classification; Generalization error bounds; Statistical learning theory BibRef

Su, L.H.[Li-Hong],
Optimizing support vector machine learning for semi-arid vegetation mapping by using clustering analysis,
PandRS(64), No. 4, July 2009, pp. 407-413.
Elsevier DOI Link
WWW Version. 0907
Classification; Training; Data mining; Land cover; Vegetation BibRef

Choi, Y.S.[Young-Sik],
Least squares one-class support vector machine,
PRL(30), No. 13, 1 October 2009, pp. 1236-1240,.
Elsevier DOI Link
WWW Version. 0909
LS (least squares) one-class SVM; Proximity measure; Relevance ranking; One-class SVM (support vector machine) 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 Link
WWW Version. 0910
Lagrangian; Support vector machines; Incremental learning; Online learning BibRef

Tuia, D., Pacifici, F.[Fabio], Kanevski, M., Emery, W.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 Link 0911
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 Link
WWW Version. 0911
Sparse classifier; Least squares support vector machine; Support vector automatic detection BibRef

Duan, L.X.[Li-Xin], Tsang, I.W.[Ivor W.], Xu, D.[Dong], Maybank, S.J.[Stephen J.],
Domain Transfer SVM for video concept detection,
CVPR09(1375-1381).
IEEE DOI Link 0906
For cross-domain learning. BibRef

Lu, Z.W.[Zhi-Wu], Ip, H.H.S.[Horace H.S.],
Image categorization by learning with context and consistency,
CVPR09(2719-2726).
IEEE DOI Link 0906
Inter-image context and cluster consistency. BibRef

Lu, Z.W.[Zhi-Wu], Ip, H.H.S.[Horace H.S.],
Image categorization with spatial mismatch kernels,
CVPR09(397-404).
IEEE DOI Link 0906
spatial mismatch kernels for use with SVM classification. BibRef

Deng, Z.J.[Zi-Jian], Li, B.C.[Bi-Cheng], Zhuang, J.[Jun],
Image Object Recognition by SVMs and Evidence Theory,
CIVR05(560-567).
Springer DOI Link 0507
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Basak, J.[Jayanta],
A least square kernel machine with box constraints,
ICPR08(1-4).
IEEE DOI Link 0812
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Fu, S.[Siyao], Guo, S.Y.[Sheng-Yang], Hou, Z.G.[Zeng-Guang], Liang, Z.Z.[Zi-Ze], Tan, M.[Min],
Multiple kernel learning from sets of partially matching image features,
ICPR08(1-4).
IEEE DOI Link 0812
SVM with multiple kernels. BibRef

Alpcan, T.[Tansu], Bauckhage, C.[Christian],
A discrete-time parallel update algorithm for distributed learning,
ICPR08(1-4).
IEEE DOI Link 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 Link 0812
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Nishida, K.[Kenji], Kurita, T.[Takio],
RANSAC-SVM for large-scale datasets,
ICPR08(1-4).
IEEE DOI Link 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).
IEEE DOI Link 0812
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Mao, W.T.[Wen-Tao], Dong, L.L.[Long-Lei], Zhang, G.[Gang],
Weighted solution path algorithm of support vector regression for abnormal data,
ICPR08(1-4).
IEEE DOI Link 0812
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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 Link 0812
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Kim, P.J.[Pyo Jae], Chang, H.J.[Hyung Jin], Choi, J.Y.[Jin Young],
Fast incremental learning for one-class support vector classifier using sample margin information,
ICPR08(1-4).
IEEE DOI Link 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 Link 0812
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Yu, X.D.[Xiao-Dong], DeMenthon, D.[Daniel], Doermann, D.[David],
Support Vector Data Description for image categorization from Internet images,
ICPR08(1-4).
IEEE DOI Link 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 Link 0812
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Han, D.Q.A.[De-Qi-Ang], Han, C.[Chongzhao], Yang, Y.[Yi], Liu, Y.[Yu], Mao, W.[Wentao],
Pre-extracting method for SVM classification based on the non-parametric K-NN rule,
ICPR08(1-4).
IEEE DOI Link 0812
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Deselaers, T.[Thomas], Heigold, G.[Georg], Ney, H.[Hermann],
SVMs, Gaussian mixtures, and their generative/discriminative fusion,
ICPR08(1-4).
IEEE DOI Link 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).
IEEE DOI Link 0812
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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).
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See also New Statistical Similarity Measure for Change Detection in Multitemporal SAR Images and Its Extension to Multiscale Change Analysis, A. BibRef

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).
Springer DOI Link 0812
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Fu, Z.Y.[Zhou-Yu], Robles-Kelly, A.[Antonio],
An instance selection approach to Multiple instance Learning,
CVPR09(911-918).
IEEE DOI Link 0906
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Earlier:
Fast multiple instance learning via L1,2 logistic regression,
ICPR08(1-4).
IEEE DOI Link 0812
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And:
On Mixtures of Linear SVMs for Nonlinear Classification,
SSPR08(489-499).
Springer DOI Link 0812
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Díaz-Chito, K.[Katerine], Ferri, F.J.[Francesc J.], Díaz-Villanueva, W.[Wladimiro],
An Empirical Evaluation of Common Vector Based Classification Methods and Some Extensions,
SSPR08(977-985).
Springer DOI Link 0812
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Schnitzspan, P.[Paul], Fritz, M.[Mario], Roth, S.[Stefan], Schiele, B.[Bernt],
Discriminative structure learning of hierarchical representations for object detection,
CVPR09(2238-2245).
IEEE DOI Link 0906
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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).
Springer DOI Link 0810
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Demirkesen, C.[Can], Cherifi, H.[Hocine],
A Comparison of Multiclass SVM Methods for Real World Natural Scenes,
ACIVS08(xx-yy).
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Hazan, T.[Tamir], Man, A.[Amit], Shashua, A.[Amnon],
A Parallel Decomposition Solver for SVM: Distributed dual ascend using Fenchel Duality,
CVPR08(1-8).
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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 Link 0806
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Li, Y.P.[Yun-Peng], Huttenlocher, D.P.[Daniel P.],
Learning for Optical Flow Using Stochastic Optimization,
ECCV08(II: 379-391).
Springer DOI Link
PDF Version. 0810
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Earlier:
Learning for stereo vision using the structured support vector machine,
CVPR08(1-8).
IEEE DOI Link 0806
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Lucey, S.[Simon],
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CVPR08(1-8).
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Sluzhivoy, A.[Andrey], Pauli, J.[Josef], Rölke, V.[Volker], Noglik, A.[Anastasia],
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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,
Visual07(136-145).
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Pao, H.T.[Hsiao T.], Xu, Y.Y.[Yeong Y.], Chuang, S.C.[Shun C.], Fu, H.C.[Hsin C.],
Image Classification and Indexing by EM Based Multiple-Instance Learning,
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Support Kernel Machines for Object Recognition,
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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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Mejía-Guevara, I.[Iván], Kuri-Morales, Á.[Ángel],
MP-Polynomial Kernel for Training Support Vector Machines,
CIARP07(584-593).
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Farrús, M.[Mireia], Ejarque, P.[Pascual], Temko, A.[Andrey], Hernando, J.[Javier],
Histogram Equalization in SVM Multimodal Person Verification,
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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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Hansen, M.S.[Michael Sass], Sjöstrand, K.[Karl], Ólafsdóttir, H.[Hildur], Larsson, H.B.W.[Henrik B. W.], Stegmann, M.B.[Mikkel B.], Larsen, R.[Rasmus],
Robust Pseudo-hierarchical Support Vector Clustering,
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González, J.[Javier], Muñoz, A.[Alberto],
Representing Functional Data Using Support Vector Machines,
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Muñoz, A.[Alberto], González, J.[Javier], de Diego, I.M.[Isaac Martín],
Local Linear Approximation for Kernel Methods: The Railway Kernel,
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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).
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Tanaka, A.[Akira], Imai, H.[Hideyuki], Kudo, M.[Mineichi], Miyakoshi, M.[Masaaki],
Optimal Kernel in a Class of Kernels with an Invariant Metric,
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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,
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Joachims, T.[Thorsten],
Structured Output Prediction with Support Vector Machines,
SSPR06(1-7).
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Zhu, Y.S.[Yong-Sheng], Yang, J.Y.[Jun-Yan], Ye, J.[Jian], Zhang, Y.Y.[You-Yun],
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Zhang, R., Metaxas, D.,
RO-SVM: Support Vector Machine with Reject Option for Image Categorization,
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Tzotsos, A.,
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Liu, Y.[Yi], Zheng, Y.F.[Yuan F.],
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Dmitry, K.[Kropotov], Nikita, P.[Ptashko], Oleg, V.[Vasiliev], Dmitry, V.[Vetrov],
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ICPR06(IV: 233-236).
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Qin, J.Z.[Jian-Zhao], Li, Y.Q.[Yuan-Qing],
An Improved Semi-Supervised Support Vector Machine Based Translation Algorithm for BCI Systems,
ICPR06(I: 1240-1243).
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Chen, G.Y., Bhattacharya, P.,
Function Dot Product Kernels for Support Vector Machine,
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Ye, N.[Ning], Sun, R.[Ruixiang], Liu, Y.G.[Yin-Gan], Cao, L.[Lin],
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Barakat, N.[Nahla], Bradley, A.P.[Andrew P.],
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ICPR06(II: 812-815).
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Zhang, X.F.[Xian-Fei], Li, B.C.[Bi-Cheng], Shi, W.[Wang], Cheng, L.[Luo],
An Efficient SVM Classifier for Lopsided Corpora,
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Sung, E.[Eric], Yan, Z.[Zhu], Li, X.C.[Xu-Chun],
Accelerating the SVM Learning for Very Large Data Sets,
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Wu, Z.L.[Zhi-Li], Li, C.H.[Chun-Hung], Zhu, J.[Ji], Huang, J.[Jian],
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Arreola, K.Z.[Karina Zapien], Fehr, J.[Janis], Burkhardt, H.[Hans],
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Osadchy, M.[Margarita], Keren, D.[Daniel],
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Brun, A.[Anders], Westin, C.F.[Carl-Fredrik], Herberthson, M.[Magnus], Knutsson, H.[Hans],
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Zhan, Y.Q.A.[Yi-Qi-Ang], Shen, D.G.[Ding-Gang],
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Tung, J.W.[Jia-Wen], Hsu, C.T.[Chiou-Ting],
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Fan, Z.G.[Zhi-Gang], Lu, B.L.[Bao-Liang],
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Yu, W.M.[Wei Miao], Du, T.H.[Tie-Hua], Lim, K.B.[Kah Bin],
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Zhang, G.X.[Ge-Xiang], Jin, W.D.[Wei-Dong], Hu, L.Z.[Lai-Zhao],
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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,
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Martinetz, T.[Thomas],
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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).
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Imbault, F., Lebart, K.,
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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,
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Lebrun, G., Charrier, C., Cardot, H.,
SVM training time reduction using vector quantization,
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Chen, J.H.[Jiun-Hung],
M-estimator based robust kernels for support vector machines,
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Gokcen, I., Joachim, D., Deller, J.R.,
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Zhang, P.[Peng], Peng, J.[Jing], Riedel, N.,
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Zhang, P.[Peng], Peng, J.[Jing],
Efficient Regularized Least Squares Classification,
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And:
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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,
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Award, Longuet-Higgins. (Awarded 10 years later for contributions that withstood the test of time.) Similar to Poggio architecture except S.V.M. for large sets of data. Maximize margin between clusters. Similar results to Poggio except higher false positives, but faster. BibRef

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