A Mixed Parallel Perceptron Classifier and Several Application problems

This paper first decomposes an n-class problem into n two-class problems, and then uses n single-output perceptrons to solve them one by one. A single-output perceptron is responsive for forming the decision boundaries of its represented class, and trained only by the samples from the represented class and some neighboring ones. The perceptrons thus have to face with such unfavorable situations as unequal number of samples between two classes, locally sparse and weak distributions, and a tiny part of strange samples. One of solutions is that the samples from the smaller sides or located in the thin regions are virtually reinforced by enlargement factors. And next, the signs of a tiny part of the mislabeled samples are simply changed The experimental results for the IRIS and handwritten digit recognitions show that the proposed methods are effective.