A Random Sampling Technique for Training Support Vector Machines
Authors: José Balcázar, Yang Dai, and Osamu Watanabe .
Source: Lecture Notes in Artificial Intelligence Vol. 2225, 2001, 119 - 134.
Abstract. Random sampling techniques have been developed for combinatorial optimization problems. In this note, we report an application of one of these techniques for training support vector machines (more precisely, primal-form maximal-margin classifiers) 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.
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