Task decomposition based on class relations: A modular neural network architecture for pattern classification

Author(s):  
Bao-Liang Lu ◽  
Masami Ito
1999 ◽  
Vol 09 (06) ◽  
pp. 563-581 ◽  
Author(s):  
KE CHEN ◽  
HUISHENG CHI

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.


2001 ◽  
Vol 38-40 ◽  
pp. 797-805 ◽  
Author(s):  
Eimei Oyama ◽  
Arvin Agah ◽  
Karl F. MacDorman ◽  
Taro Maeda ◽  
Susumu Tachi

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