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