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An iterative algorithm learning the maximal margin classifier

✍ Scribed by Vojtěch Franc; Václav Hlaváč

Publisher
Elsevier Science
Year
2003
Tongue
English
Weight
444 KB
Volume
36
Category
Article
ISSN
0031-3203

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✦ Synopsis

A simple learning algorithm for maximal margin classiÿers (also support vector machines with quadratic cost function) is proposed. We build our iterative algorithm on top of the Schlesinger-Kozinec algorithm (S-K-algorithm) from 1981 which ÿnds a maximal margin hyperplane with a given precision for separable data. We suggest a generalization of the S-K-algorithm (i) to the non-linear case using kernel functions and (ii) for non-separable data. The requirement in memory storage is linear to the data. This property allows the proposed algorithm to be used for large training problems.

The resulting algorithm is simple to implement and as the experiments showed competitive to the state-of-the-art algorithms. The implementation of the algorithm in Matlab is available. We tested the algorithm on the problem aiming at recognition poor quality numerals.

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