SVM vs Regularized Least Squares Classification

Authors:
Peng Zhang;Jing Peng
Affiliations:
Tulane University, New Orleans;Tulane University, New Orleans
Venue:
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Year:
2004

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Abstract

Support vector machines (SVMs) and regularized least squares (RLS) are two recent promising techniques for classification. SVMs implement the structure risk minimization principle and use the kernel trick to extend it to the non-linear case. On the other hand, RLS minimizes a regularized functional directly in a reproducing kernel Hilbert space defined by a kernel. While both have a sound mathematical foundation, RLS is strikingly simple. On the other hand, SVMs in general have a sparse representation of solutions. In addition, the performance of SVMs has been well documented but little can be said of RLS. This paper applies these two techniques to a collection of data sets and presents results demonstrating virtual identical performance by the two methods.