14.2.15 Support Vector Machines, SVM

Schölkopf, B.[Bernhard],
Support Vector Machines,
Oldenbourg Verlag: Munich, 1997. BibRef 9700

Schölkopf, B.[Bernhard],
Support Vector Learning,
R. Oldenbourg VerlagMunich, 1997.
WWW Version. BibRef 9700

Cristianini, N.[Nello], Shawe-Taylor, J.[John],
An Introduction to Support Vector Machines,
Cambridge University Press2000. Survey, SVM.
WWW Version. ISBN: 0 521 78019 5 To purchase this book look here BibRef 0001

Chang, C.C., and Lin, C.J.,
LIBSVM: a library for support vector machines,
Online2001. Code, SVM. Software available:
WWW Version. BibRef 0100

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. 9709Given 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).
WWW Version. 9909 BibRef

Chapelle, O., Haffner, P., Vapnik, V.,
Support Vector Machines for Histogram-Based Image Classification,
TNN(10), No. 5, May 1999, pp. 1055-1064. BibRef 9905

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

Scholkopf, B.[Bernhard], Smola, A.J.[Alexander J.], Muller, K.R.[Klaus-Robert], Bartlett, P.L.,
New Support Vector Algorithms,
NeurComp(12), 2000, pp. 1207-1245. BibRef 0001

Cristianini, N.[Nello], Schölkopf, B.[Bernhard],
Support Vector Machines and Kernel Methods: The New Generation of Learning Machines,
AIMag(23), No. 3, Fall 2002, pp. 31-41. Survey, SVM. Survey and general discussion. BibRef 0200

Kienzle, W.[Wolf], Bakir, G.H.[Gökhan H.], Franz, M.O.[Matthias O.], Schölkopf, B.[Bernhard],
Efficient Approximations for Support Vector Machines in Object Detection,
DAGM04(54-61).
WWW Version. 0505 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.
WWW Version. 0702Unify all kernel learning approaches. BibRef

Chen, S., Gunn, S., Harris, C.J.,
Decision feedback equaliser design using support vector machines,
VISP(147), No. 3, 2000, pp. 213-219. 0008 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.
WWW Version. 0512 BibRef

Pedroso, J.P.[Joăo 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], Vitriŕ, 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., Zhou, W., Jiao, L.,
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.[Wenfang],
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

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).
WWW Version. 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. 0711Time 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

Zhan, H.[Haigang], Shi, P.[Ping], Chen, C.[Chuqun],
Retrieval of oceanic chlorophyll concentration using support vector machines,
GeoRS(41), No. 12, December 2003, pp. 2947-2951.
IEEE Abstract. IEEE Top Reference. 0402 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. 0402Rather 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).
WWW Version. 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.[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 Version. 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 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.
WWW Version. 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. 0611Multi-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).
WWW Version. 0211 BibRef

Haasdonk, B.[Bernard], Bahlmann, C.[Claus],
Learning with Distance Substitution Kernels,
DAGM04(220-227).
WWW Version. 0505 BibRef

Shin, H.[Hyunjung], 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. 0501SVM 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. 0611Model 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., Catalŕ, 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., Catalŕ, A.,
Dual unification of bi-class support vector machine formulations,
PR(39), No. 7, July 2006, pp. 1325-1332.
WWW Version. 0606Large margin principle; Optimization; Convex hull BibRef

Lee, K.[KiYoung], 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

Muńoz, 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.
WWW Version. 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. 0605Early 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).
WWW Version. 0409Kernel 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. 0704Support 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.
WWW Version. 0606Identify the uncertain samples. 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. 0608Multi-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. 0611Incremental 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. 0611Support 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.
WWW Version. 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.
WWW Version. 0611 BibRef
Earlier: A2, A1, A3:
A Sparse Support Vector Machine Approach to Region-Based Image Categorization,
CVPR05(I: 1121-1128).
WWW Version. 0507Training 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.
WWW Version. 0704A 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. 0705Support 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.
WWW Version. 0611 BibRef

Bazi, Y., Melgani, F.,
Semisupervised PSO-SVM Regression for Biophysical Parameter Estimation,
GeoRS(45), No. 6, June 2007, pp. 1887-1895.
WWW Version. 0706 BibRef

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

Chuang, C.C.,
Fuzzy Weighted Support Vector Regression With a Fuzzy Partition,
SMC-B(37), No. 3, June 2007, pp. 630-640.
WWW Version. 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. 0709Support vector machine; String kernel; Classification BibRef

Munoz-Mari, J., Bruzzone, L., Camps-Valls, G.,
A Support Vector Domain Description Approach to Supervised Classification of Remote Sensing Images,
GeoRS(45), No. 8, August 2007, pp. 2683-2692.
WWW Version. 0709 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. 0709Image 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.
WWW Version. 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. 0711Support 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.
WWW Version. 0801 BibRef

Ye, W.[Wang], Shang-Teng, H.[Huang],
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. 0711SVM; 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.
WWW Version. 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. 0711Support 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.
WWW Version. 0711Transductive 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. 0711Support vector machine; Support vector regression; Data reduced method; Cross validation; k-Nearest neighbor BibRef

Angelini, L.[Leonardo], Marinazzo, D.[Daniele], Pellicoro, M.[Mario], Stramaglia, S.[Sebastiano],
Semi-supervised learning by search of optimal target vector,
PRL(29), No. 1, 1 January 2008, pp. 34-39.
WWW Version. 0711Semi-supervised learning; Kernel principal components; Transductive inference 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. 0802Multiclass pattern classification; Computational load reduction; Support vector learning BibRef

Malon, C.[Christopher], Uchida, S.[Seiichi], Suzuki, M.[Masakazu],
Mathematical symbol recognition with support vector machines,
PRL(29), No. 9, 1 July 2008, pp. 1326-1332.
WWW Version. 0711 BibRef
Earlier:
Support Vector Machines for Mathematical Symbol Recognition,
SSPR06(136-144).
WWW Version. 0608Support vector machine; OCR; Mathematical document; Mathematical symbol recognition 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. 0711Relevance vector machine; Unbalanced data; Noise; Fuzzy membership; Bayesian inference BibRef

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).
WWW Version. 0706 BibRef

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,
Visual07(146-153).
WWW Version. 0706 BibRef

Kumar, A.[Ankita], Sminchisescu, C.[Cristian],
Support Kernel Machines for Object Recognition,
ICCV07(1-8).
WWW Version. 0710 BibRef

Orabona, F., Castellini, C., Caputo, B., Luo, J., Sandini, G.,
Indoor Place Recognition using Online Independent Support Vector Machines,
BMVC07(xx-yy).
PDF Version. 0709 BibRef

Hernández, N.[Noslen], Biscay, R.J.[Rolando J.], Talavera, I.[Isneri],
Support Vector Regression Methods for Functional Data,
CIARP07(564-573).
WWW Version. 0711 BibRef

Moguerza, J.M.[Javier M.], Muńoz, A.[Alberto], Psarakis, S.[Stelios],
Monitoring Nonlinear Profiles Using Support Vector Machines,
CIARP07(574-583).
WWW Version. 0711 BibRef

Mejía-Guevara, I.[Iván], Kuri-Morales, Á.[Ángel],
MP-Polynomial Kernel for Training Support Vector Machines,
CIARP07(584-593).
WWW Version. 0711 BibRef

Farrús, M.[Mireia], Ejarque, P.[Pascual], Temko, A.[Andrey], Hernando, J.[Javier],
Histogram Equalization in SVM Multimodal Person Verification,
ICB07(819-827).
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Wang, L.[Lei],
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ACCV07(II: 145-154).
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Chatelain, C., Adam, S., Lecourtier, Y., Heutte, L., Paquet, T.,
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ICDAR07(427-431).
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Karim, R.[Rezaul], Bergtholdt, M.[Martin], Kappes, J.[Jörg], Schnörr, C.[Christoph],
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Qin, J.[Jianzhao], 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.,
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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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Zhang, X.F.[Xian-Fei], Li, B.C.[Bi-Cheng], Shi, W.[Wang], Cheng, L.[Luo],
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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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Liang, Z.Z.[Zhi-Zheng], Zhao, T.[Tuo],
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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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Chen, G.Y.[Guang-Yi], Dudek, G.[Gregory],
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Zhan, Y.Q.[Yi-Qiang], 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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Lyu, S.W.[Si-Wei],
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Sun, Q.[Qiang], DeJong, G.[Gerald],
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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],
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ICIP04(III: 1545-1548).
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Martinetz, T.[Thomas],
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Horikawa, Y.[Yo],
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Park, J.H.[Jin-Hyeong], Ji, X.[Xiang], Zha, H.Y.[Hong-Yuan], Kasturi, R.,
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Imbault, F., Lebart, K.,
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ICPR04(IV: 597-600).
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Hoi, C.H.[Chu-Hong], Lyu, M.R.,
Group-based relevance feedback with support vector machine ensembles,
ICPR04(III: 874-877).
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Lebrun, G., Charrier, C., Cardot, H.,
SVM training time reduction using vector quantization,
ICPR04(I: 160-163).
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Li, M.K.[Ming-Kun], Sethi, I.K.[Ishwar K.],
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Chen, J.H.[Jiun-Hung],
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ICPR04(I: 168-171).
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Gokcen, I., Joachim, D., Deller, J.R.,
Comparing optimal bounding ellipsoid and support vector machine active learning,
ICPR04(I: 172-175).
WWW Version. 0409 BibRef

Zhang, P.[Peng], Peng, J.[Jing], Riedel, N.,
Discriminant Analysis: A Least Squares Approximation View,
LCV05(III: 46-46).
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Zhang, P.[Peng], Peng, J.[Jing],
Efficient Regularized Least Squares Classification,
LCV04(98).
WWW Version. 0406 BibRef
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SVM vs regularized least squares classification,
ICPR04(I: 176-179).
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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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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).
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Wang, L.[Lei], Xue, P.[Ping], Chan, K.L.[Kap Luk],
Incorporating prior knowledge into SVM for image retrieval,
ICPR04(II: 981-984).
WWW Version. 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).
WWW Version. 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).
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Yuan, C.[Chao], Casasent, D.,
A novel support vector classifier with better rejection performance,
CVPR03(I: 419-424).
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Wolf, L., Shashua, A.,
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ICCV03(378-384).
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Kernel principal angles for classification machines with applications to image sequence interpretation,
CVPR03(I: 635-640).
IEEE Abstract. IEEE Top Reference. 0307 BibRef

Sahbi, H.[Hichem], Fleuret, F.[François],
Scale-Invariance of Support Vector Machines based on the Triangular Kernel,
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Vishwanathan, S.V.N., Murty, M.N.,
Geometric SVM: a fast and intuitive SVM algorithm,
ICPR02(II: 56-59).
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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).
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Franc, V., Hlavac, V.,
Multi-class support vector machine,
ICPR02(II: 236-239).
WWW Version. 0211 BibRef

Zhang, J., Zhang, Y., Zhou, T.,
Classification of Hyperspectral Data Using Support Vector Machine,
ICIP01(I: 882-885).
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Nakamura, E., Murayama, N., Sawada, K., Okuizumi, H.,
RLGS Profile Segmentation Via a SVM,
ICIP01(I: 533-536).
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Hermes, L., Buhmann, J.M.,
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ICPR00(Vol II: 712-715).
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Ben-Hur, A., Horn, D., Siegelmann, H.T., Vapnik, V.,
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ICPR00(Vol II: 724-727).
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Yang, M.H.[Ming-Hsuan], Ahuja, N.[Narendra],
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CVPR00(I: 430-437).
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Osuna, E., Freund, R., Girosi, F.,
Training Support Vector Machines: An Application to Face Detection,
CVPR97(130-136).
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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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Pawlak, M., Siedlecki, W.,
Kernel classification rules in the presence of missing values,
ICPR90(I: 677-680).
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Robust Techniques, Robust Classification .