NOTE: The following materials are presented for timely dissemination of academic and technical work. Copyright and all other rights therein are reserved by authors and/or other copyright holders. Persoanl use of the following materials is permitted and, however, people using the materials or information are expected to adhere to the terms and constraints invoked by the related copyright.

A Modular Neural Network Architecture for Pattern Classification Based on Different Feature Sets


We propose a novel connectionist method for the use of different feature sets in pattern classification. Unlike traditional methods, e.g. combination of multiple classifiers and use of a composite feature set, our method copes with the problem based on an idea of soft competition on different feature sets developed in our earlier work. An alternative modular neural network architecture is proposed to provide a more effective implementation of soft competition on different feature sets. The proposed architecture is interpreted as a generalized finite mixture model and, therefore, parameter estimation is treated as a maximum likelihood problem. An EM algorithm is derived for parameter estimation and, moreover, a model selection method is proposed to fit the proposed architecture to a specific problem. Comparative results are presented for the real world problem of speaker identification.

Click ijns99.pdf for full text