Abstract
The min-max modular network has been shown to be an efficient classifier, especially in solving large-scale and complex pattern classification problems. Despite its high modularity and parallelism, it suffers from quadratic complexity in space when a multiple-class problem is decomposed into a number of linearly separable problems. This paper proposes two new pruning methods and an integrated process to reduce the redundancy of the network and optimize the network structure. We show that our methods can prune a lot of redundant modules in comparison with the original structure while maintaining the generalization accuracy.
This work was supported in part by the National Natural Science Foundation of China via the grants NSFC 60375022 and NSFC 60473040.
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Yang, Y., Lu, B. (2005). Structure Pruning Strategies for Min-Max Modular Network. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_103
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DOI: https://doi.org/10.1007/11427391_103
Publisher Name: Springer, Berlin, Heidelberg
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