A Multi-Sieving Neural Network Architecture That Decomposes Learning Tasks Automatically
نویسنده
چکیده
This paper presents a multi-sieving network (MSN) architecture and a multi-sieving learning (MSL) algorithm for it. The basic idea behind MSN architecture is the multi-sieve method, that is, patterns are classified by a rough sieve at the beginning and done by finer ones gradually. MSN is constructed by adding a sieving module (SM) adaptively with progress of training. SM consists of two different neural networks and a simple logical circuit. MSL algorithm starts with a single SM, then does the following three phases repeatedly until all the training samples are successfully learned: (a) the learning phase in which the training samples are learned by the current SM, (b) the sieving phase in which the training samples that have been successfully learned are sifted out from the training set, and (c) the growing phase in which the current SM is frozen and a new S M is added in order to learn the remaining training samples. MSN architecture has several attractive properties such as automatic decomposition of learning tasks, modular structure, easy implementation of additional learning, overcoming a problem of local minima and fast convergence. The performance of MSN architecture is illustrated on two benchmark problems.
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