Typical sample selection and redundancy reduction for min-max modular network with GZC function

Authors:
Jing Li;Baoliang Lu;Michinori Ichikawa
Affiliations:
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Lab. for Brain-Operative Device, RIKEN Brain Science Institue, Saitama, Japan
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Year:
2005

Citing 3
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An algorithm for pruning redundant modules in min-max modular network with GZC function

ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I

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Abstract

The min-max modular neural network with Gaussian zero-crossing function (M3-GZC) has locally tuned response characteristic and emergent incremental learning ability, but it suffers from high storage requirement. This paper presents a new algorithm, called Enhanced Threshold Incremental Check (ETIC), which can select representative samples from new training data set and can prune redundant modules in an already trained M3-GZC network. We perform experiments on an artificial problem and some real-world problems. The results show that our ETIC algorithm reduces the size of the network and the response time while maintaining the generalization performance.