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One of keys for multilayer perceptrons (MLPs) to solve the multi-class learning problems is how to make them get good convergence and generalization performances merely through learning small-scale subsets, i.e., a small part of the original larger-scale data sets. This paper first decomposes an n-class problem into n two-class problems, and then uses n class-modular MLPs to solve them one by one...
A complicated learning problem can be decomposed into multiple simple two-class problems. A single-output classifier for separating its represented class from the others really solves a two-class problem, and can be trained by all samples from the represented class and a small part from its neighboring classes. The equal sample sizes in the two-class problems thus come into being. Two of the solutions...
This paper decomposes a large-scale learning problem into multiple limited-scale pairs of training subsets and cross validation (CV) subsets. One training subset only consists of its own class and some most neighboring samples from the other categories. Naturally, modular multilayer perceptrons (MLPs) come into being. If the final decision region of an MLP is open, its real outputs must be amended...
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