.png){kind=icon}
返回
An efficient method to construct a radial basis function neural network classifier
.png){kind=icon}
PRE
AI
DOI：10.1016/S0893-6080(97)00002-6.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Radial basis function neural network (RBFN) has the power of the universal function approximation But how to construct an RBFN to solve a given problem is usually not straightforward. This paper describes a method to construct an RBFN classifier efficiently and effectively. The method determines the middle layer neurons by a fast clustering algorithm and computes the optimal weights between the middle and the output layers statistically. We applied the proposed method to construct an RBFN classifier for an unconstrained handwritten digit recognition. The experiment showed that the method could construct an RBFN classifier quickly and the performance of the classifier was better than the best result previously reported. (C) 1997 Elsevier Science Ltd.
Keyword:
radial basis function
linear discriminant function
classification
APC-III
clustering
GRBF
LMS
handwritten digit recognition
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.3
论文数:
7.8K
被引数:
3.0W
机构
暂无机构信息
引用论文
A hysteresis loop in electrical resistance of NbHx observed above the β−λ transition temperature.png)
AIP Advances
OAAI
AIP AdvancesIF0
OAAI分享收藏Chiral occlusion in two-dimensional binary supramolecular networks studied by the Monte Carlo methodCrystEngComm
CrystEngCommIF0
PREAI
分享收藏
查看更多内容