A Guide to Support Vector Machines
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[1] Don R. Hush,et al. Polynomial-Time Decomposition Algorithms for Support Vector Machines , 2003, Machine Learning.
[2] Chih-Jen Lin,et al. On the convergence of the decomposition method for support vector machines , 2001, IEEE Trans. Neural Networks.
[3] R. Fletcher. Practical Methods of Optimization , 1988 .
[4] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[5] Gérard Dreyfus,et al. Single-layer learning revisited: a stepwise procedure for building and training a neural network , 1989, NATO Neurocomputing.
[6] Nello Cristianini,et al. An introduction to Support Vector Machines , 2000 .
[7] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[8] Thomas M. Cover,et al. Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition , 1965, IEEE Trans. Electron. Comput..
[9] John C. Platt,et al. Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .
[10] Chih-Jen Lin,et al. IJCNN 2001 challenge: generalization ability and text decoding , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).
[11] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[12] S. Sathiya Keerthi,et al. Improvements to Platt's SMO Algorithm for SVM Classifier Design , 2001, Neural Computation.
[13] Thorsten Joachims,et al. Making large scale SVM learning practical , 1998 .
[14] Mokhtar S. Bazaraa,et al. Nonlinear Programming: Theory and Algorithms , 1993 .
[15] Federico Girosi,et al. Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[16] Chih-Jen Lin,et al. The analysis of decomposition methods for support vector machines , 2000, IEEE Trans. Neural Networks Learn. Syst..
[17] Chih-Jen Lin,et al. Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.
[18] Ulrich H.-G. Kreßel,et al. Pairwise classification and support vector machines , 1999 .
[19] Chih-Jen Lin,et al. A formal analysis of stopping criteria of decomposition methods for support vector machines , 2002, IEEE Trans. Neural Networks.
[20] Chih-Jen Lin,et al. Formulations of Support Vector Machines: A Note from an Optimization Point of View , 2001, Neural Computation.
[21] Ke Wang,et al. PSORT-B: improving protein subcellular localization prediction for Gram-negative bacteria , 2003, Nucleic Acids Res..
[22] Alexander J. Smola,et al. Learning with kernels , 1998 .
[23] S. Sathiya Keerthi,et al. Convergence of a Generalized SMO Algorithm for SVM Classifier Design , 2002, Machine Learning.
[24] David J. Spiegelhalter,et al. Machine Learning, Neural and Statistical Classification , 2009 .
[25] Chih-Jen Lin,et al. Asymptotic convergence of an SMO algorithm without any assumptions , 2002, IEEE Trans. Neural Networks.