A POLYNOMIAL-TIME ALGORITHM FOR THE CONSTRUCTION AND TRAINING OF A CLASS OF MULTILAYER PERCEPTRONS

被引：54
|
作者：
ROY, A
KIM, LS
MUKHOPADHYAY, S
机构：
[1] Arizona State Univ, Tempe, United States
来源：
NEURAL NETWORKS | 1993年 / 6卷 / 04期
基金：
美国国家科学基金会;
关键词：
POLYNOMIAL TIME ALGORITHM; MULTILAYER PERCEPTRONS; LINEAR PROGRAMMING; CLASSIFICATION ALGORITHM; SUPERVISED LEARNING; CLUSTERING; NET DESIGN;
D O I：
10.1016/S0893-6080(05)80057-7
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 presents a polynomial time algorithm for the construction and training of a class of multilayer perceptrons for classification. It uses linear programming models to incrementally generate the hidden layer in a restricted higher-order perceptron. Polynomial time complexity of the method is proven. Computational results are provided for several well-known applications in the areas of speech recognition, medical diagnosis, and target detection. In all cases, very small nets were created that had error rates similar to those reported so far.
引用
收藏
页码：535 / 545
页数：11
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