Typical Sample Selection and Redundancy Reduction for Min-Max Modular Network with GZC Function
نویسندگان
چکیده
The min-max modular neural network with Gaussian zerocrossing function (M-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 M-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.
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An Algorithm for Pruning Redundant Modules in Min-Max Modular Network with GZC Function
The min-max modular neural network with Gaussian zerocrossing function (M-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 prop...
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