SVMTorch: Support Vector Machines for Large-Scale Regression Problems

Keywords: learning Reference EPFL-REPORT-82604 URL: http://publications.idiap.ch/downloads/reports/2000/rr00-17.pdf Record created on 2006-03-10, modified on 2017-05-10

[1]  R. Fletcher.  Practical Methods of Optimization , 1988 .

[2]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[3]  Federico Girosi,et al.  An improved training algorithm for support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[4]  Gunnar Rätsch,et al.  Predicting Time Series with Support Vector Machines , 1997, ICANN.

[5]  Thorsten Joachims,et al.  Making large-scale support vector machine learning practical , 1999 .

[6]  Pavel Laskov,et al.  An Improved Decomposition Algorithm for Regression Support Vector Machines , 1999, NIPS.

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

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

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

[10]  Samy Bengio,et al.  On the Convergence of SVMTorch, an Algorithm for Large-Scale Regression Problems , 2000 .

[11]  S. Sathiya Keerthi,et al.  Improvements to the SMO algorithm for SVM regression , 2000, IEEE Trans. Neural Networks Learn. Syst..

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

[13]  S. Sathiya Keerthi,et al.  Improvements to Platt's SMO Algorithm for SVM Classifier Design , 2001, Neural Computation.