The Support Vector Method of Function Estimation
暂无分享,去创建一个
This chapter describes the Support Vector technique for function estimation problems such as pattern recognition, regression estimation, and solving linear operator equations. It shows that for the Support Vector method both the quality of solution and the complexity of the solution does not depend directly on the dimensionality of an input space. Therefore, on the basis of this technique one can obtain a good estimate using a given number of high-dimensional data.
[1] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[2] Alexander J. Smola,et al. Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.
[3] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[4] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.