TITLE: Ensemble MLP Classifier Design
SPEAKER: Terry Windeatt (Department of Electronic Engineering, University of Surrey)

ABSTRACT:

The idea of combining multiple classifiers is based on the observation that achieving optimal performance in combination is not necessarily consistent with obtaining the best performance for a single (base) classifier. However, the base classifier parameters still need to be set, and the optimal parameters may be different for the ensemble. The normal way to set parameters is to use a validation set or cross-validation techniques. In this talk, measures for setting parameters for two-class problems will be discussed, and extended to the problem of identifying and removing irrelevant features. The technique is extended to multi-class problems using ECOC (Error-Correcting Output Coding). Examples using MLP base classifiers for face recognition will be described.