A tutorial on -support vector machines

We briefly describe the main ideas of statistical learning theory, support vector machines (SVMs), and kernel feature spaces. We place particular emphasis on a description of the so-called -SVM, in...

[1]  J. Mercer Functions of Positive and Negative Type, and their Connection with the Theory of Integral Equations , 1909 .

[2]  P. Wolfe A duality theorem for non-linear programming , 1961 .

[3]  V. Vapnik Pattern recognition using generalized portrait method , 1963 .

[4]  M. Aizerman,et al.  Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning , 1964 .

[5]  Mordecai Avriel,et al.  Nonlinear programming , 1976 .

[6]  G. Wahba Spline models for observational data , 1990 .

[7]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[8]  Mokhtar S. Bazaraa,et al.  Nonlinear Programming: Theory and Algorithms , 1993 .

[9]  David J. Spiegelhalter,et al.  Machine Learning, Neural and Statistical Classification , 2009 .

[10]  Bernhard Schölkopf,et al.  Improving the accuracy and speed of support vector learning machines , 1997, NIPS 1997.

[11]  Bernhard Schölkopf,et al.  Support vector learning , 1997 .

[12]  Noga Alon,et al.  Scale-sensitive dimensions, uniform convergence, and learnability , 1997, JACM.

[13]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[14]  John Shawe-Taylor,et al.  Generalization Performance of Support Vector Machines and Other Pattern Classifiers , 1999 .

[15]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .

[16]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[17]  B. Schölkopf,et al.  Advances in kernel methods: support vector learning , 1999 .

[18]  V. Vapnik,et al.  Bounds on Error Expectation for Support Vector Machines , 2000, Neural Computation.

[19]  Kristin P. Bennett,et al.  Duality and Geometry in SVM Classifiers , 2000, ICML.

[20]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[21]  Bernhard Schölkopf,et al.  New Support Vector Algorithms , 2000, Neural Computation.

[22]  Alexander J. Smola,et al.  Advances in Large Margin Classifiers , 2000 .

[23]  Chih-Jen Lin,et al.  On the convergence of the decomposition method for support vector machines , 2001, IEEE Trans. Neural Networks.

[24]  Arthur Gretton,et al.  Estimating the Leave-One-Out Error for Classification Learning with SVMs , 2001 .

[25]  Cheng-Chew Lim,et al.  Dual /spl nu/-support vector machine with error rate and training size biasing , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[26]  Bernhard Schölkopf,et al.  Learning with kernels , 2001 .

[27]  Chih-Jen Lin,et al.  Training v-Support Vector Classifiers: Theory and Algorithms , 2001, Neural Computation.

[28]  Chih-Jen Lin,et al.  Formulations of Support Vector Machines: A Note from an Optimization Point of View , 2001, Neural Computation.

[29]  Bernhard Schölkopf,et al.  Generalization Performance of Regularization Networks and Support Vector Machines via Entropy Numbers of Compact Operators , 1998 .

[30]  Bernhard Schölkopf,et al.  Kernel Dependency Estimation , 2002, NIPS.

[31]  Ingo Steinwart,et al.  Support Vector Machines are Universally Consistent , 2002, J. Complex..

[32]  Jitendra Malik,et al.  Learning to Detect Natural Image Boundaries Using Brightness and Texture , 2002, NIPS.

[33]  Carl Staelin,et al.  A Personal Email Assistant , 2002 .

[34]  Ingo Steinwart,et al.  Sparseness of Support Vector Machines , 2003, J. Mach. Learn. Res..

[35]  Chih-Jen Lin,et al.  Decomposition Methods for Linear Support Vector Machines , 2003, Neural Computation.

[36]  Ingo Steinwart,et al.  On the Optimal Parameter Choice for v-Support Vector Machines , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[37]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[38]  Cheng-Chew Lim,et al.  An Implementation of Training Dual-nu Support Vector Machines , 2005 .