ALCOMFT-TR-01-92

José L. Balcázar, Yang Dai and Osamu Watanabe
Random sampling techniques for training support vector machines
Barcelona. Work package 1. May 2001.

Abstract: Random sampling techniques have been developed in for combinatorial optimization problems. In this note, we report two applications of one of these techniques for training support vector machines that solve two-group classification problems by using hyperplane classifiers. Through this research, we are aiming (i) to design efficient and theoretically guaranteed support vector machine training algorithms, and (ii) to develop systematic and efficient methods for finding ``outliers'', i.e., examples having an inherent error.

Postscript file: ALCOMFT-TR-01-92.ps.gz (89 kb).