Fuzzily modular multilayer perceptron classifiers for large-scale learning problems

被引:0
|
作者
Gao, DQ [1 ]
Yang, YF [1 ]
机构
[1] E China Univ Sci & Technol, Dept Comp Sci, State Key Lab Bioreactor Engn, Shanghai 200237, Peoples R China
来源
FUZZ-IEEE 2005: PROCEEDINGS OF THE IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS: BIGGEST LITTLE CONFERENCE IN THE WORLD | 2005年
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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. According to the fuzzy set theory, each output of MLPs is added a correction coefficient, which is related to the class mean and covariance. In addition, weight increment correction factors are added to solve the sample disequilibrium problems in training subsets. The result for letter recognition shows that the above methods are quite effective.
引用
收藏
页码:625 / 630
页数:6
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