Solving multi-class pattern recognition problems with tree-structured support vector machines

9Citations
Citations of this article
7Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Support vector machines (SVM) are learning algorithms derived from statistical learning theory. The SVM approach was originally developed for binary classification problems. In this paper SVM architectures for multi-class classification problems are discussed, in particular we consider binary trees of SVMs to solve the multi-class problem. Numerical results for different classifiers on a benchmark data set of hand written digits are presented.

Cite

CITATION STYLE

APA

Schwenker, F. (2001). Solving multi-class pattern recognition problems with tree-structured support vector machines. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2191, pp. 283–290). Springer Verlag. https://doi.org/10.1007/3-540-45404-7_38

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free