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A Connectionist Method for Pattern Classification with Diverse Features
ABSTRACT
A novel connectionist method is proposed to simultaneously use diverse
features in an optimal way for pattern classification. Unlike methods
of combining multiple classifiers, a modular neural network architecture is
proposed through use of soft competition among diverse features. Parameter
estimation in the proposed architecture is treated as a maximum likelihood
problem, and an Expectation-Maximization (EM) learning algorithm is developed
for adjusting the parameters of the architecture. Comparative simulation
results are presented for the real world problem of speaker identification.
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