SVM vs regularized least squares classification

Peng Zhang, Jing Peng

Research output: Contribution to journalConference article

53 Scopus citations

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 Hubert 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.

Original languageEnglish
Pages (from-to)176-179
Number of pages4
JournalProceedings - International Conference on Pattern Recognition
Volume1
StatePublished - 17 Dec 2004
EventProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004 - Cambridge, United Kingdom
Duration: 23 Aug 200426 Aug 2004

Fingerprint Dive into the research topics of 'SVM vs regularized least squares classification'. Together they form a unique fingerprint.

  • Cite this