Boosting the HONG network

被引：8

作者：

Atukorale, AS

Downs, T

Suganthan, PN

机构：

[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore

[2] Univ Queensland, Sch Informat Technol & Elect Engn, Brisbane, Qld 4072, Australia

来源：

NEUROCOMPUTING | 2003年 / 51卷

关键词：

character recognition; pattern recognition; hierarchical overlapped architecture; multiple classifier fusion; neural-gas network; boosting; UCI database; LVQ; SOM;

D O I：

10.1016/S0925-2312(02)00603-3

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 gives a brief description of a hierarchical architecture (HONG) that has been described in Atukorale and Suganthan (Neurocomputing 35 (2000) 165). The learning algorithm it uses is a mixed unsupervised/supervised method with most of the learning being unsupervised. The architecture generates multiple classifications for every data pattern presented, and combines them to obtain the final classification. The main objective of this paper is to show how boosting can be used to improve the performance of the HONG classifier. (C) 2002 Elsevier Science B.V. All rights reserved.

引用

页码：75 / 86

页数：12

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