An evaluation of ensemble methods in handwritten word recognition based on feature selection

被引：3
|
作者：
Günter, S [1 ]
Bunke, H [1 ]
机构：
[1] Univ Bern, Dept Comp Sci, CH-3012 Bern, Switzerland
来源：
PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 1 | 2004年
关键词：
handwritten word recognition; classifier ensemble methods; feature selection; hidden Markov model (HMM);
D O I：
10.1109/ICPR.2004.1334133
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Handwritten text recognition is one of the most difficult problems in the field of pattern recognition. The combination of multiple classifiers has been proven to be able to increase the recognition rate in difficult problems when compared to single classifiers. In this paper several novel methods for the creation of classifier ensembles are compared where the individual classifiers use different feature subsets. The methods are evaluated in the context of handwritten word recognition, using a hidden Markov model recognizer as basic classifier.
引用
收藏
页码：388 / 392
页数：5
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