文献类型：期刊文献

英文题名：A mixed parallel perceptron classifier and several application problems

作者：Gao, Daqi[1]; Li, Hao[2]; Chen, Wei[2]

机构：[1] Department of Computer Science, State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China; [2] Department of Computer Science, East China Uni. of Science and Technology, Shanghai 200237, China

年份：2006

起止页码：4797

外文期刊名：IEEE International Conference on Neural Networks - Conference Proceedings

收录：EI(收录号：20081211154477)

语种：英文

外文关键词：Boundary conditions - Decision theory - Problem solving

摘要：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. ? 2006 IEEE.