论文信息 - Least Squares Support Vector Machine Classifiers

Least Squares Support Vector Machine Classifiers

In this letter we discuss a least squares version for support vector machine (SVM) classifiers. Due to equality type constraints in the formulation, the solution follows from solving a set of linear equations, instead of quadratic programming for classical SVM's. The approach is illustrated on a two-spiral benchmark classification problem.

Johan A. K. Suykens | Joos Vandewalle | J. Suykens | J. Vandewalle

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

[2] Jacek M. Zurada,et al. Introduction to artificial neural systems , 1992 .

[3] Heekuck Oh,et al. Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[4] S. Hyakin,et al. Neural Networks: A Comprehensive Foundation , 1994 .

[5] Sandro Ridella,et al. Circular backpropagation networks for classification , 1997, IEEE Trans. Neural Networks.

[6] Bernhard Schölkopf,et al. Comparing support vector machines with Gaussian kernels to radial basis function classifiers , 1997, IEEE Trans. Signal Process..

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

[8] V. Vapnik. The Support Vector Method of Function Estimation , 1998 .

[9] Alexander Gammerman,et al. Ridge Regression Learning Algorithm in Dual Variables , 1998, ICML.

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

[11] Robert A. Lordo,et al. Learning from Data: Concepts, Theory, and Methods , 2001, Technometrics.