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Erschienen in:
2005 | OriginalPaper | Buchkapitel
An Algorithm for Pruning Redundant Modules in Min-Max Modular Network with GZC Function
verfasst von : Jing Li, Bao-Liang Lu, Michinori Ichikawa
Erschienen in: Advances in Natural Computation
Verlag: Springer Berlin Heidelberg
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The min-max modular neural network with Gaussian zero-crossing function (M
3
-GZC) has locally tuned response characteristic and emergent incremental learning ability, but it suffers from quadratic complexity in storage space and response time. Redundant Sample pruning and redundant structure pruning can be considered to overcome these weaknesses. This paper aims at the latter; it analyzes the properties of receptive field in M
3
-GZC network, and then proposes a strategy for pruning redundant modules. Experiments on both structure pruning and integrated with sample pruning are performed. The results show that our algorithm reduces both the size of the network and the response time notably while not changing the decision boundaries.