An Approach to Large Margin Design of Prototype-Based Pattern Classifiers

In this paper, we propose a maximum separation margin (MSM) training method for multiple-prototype (MP)-based pattern classifiers in which a sample separation margin defined as the distance from the training sample to the classification boundary can be calculated precisely. Similar to support vector machine (SVM) methodology, MSM training is formulated as a multicriteria optimization problem which aims at maximizing the separation margin and minimizing the empirical error rate on training data simultaneously. By making certain relaxation assumptions, MSM training can be reformulated as a semidefinite programming (SDP) problem that can be solved efficiently by some standard optimization algorithms designed for SDP. Evaluation experiments are conducted on the task of the recognition of most confusable Kanji character pairs identified from popular Nakayosi and Kuchibue handwritten Japanese character databases. It is observed that the MSM-trained MP-based classifier achieves a similar character recognition accuracy as that of the state-of-the-art SVM-based classifier, yet requires much fewer classifier parameters.