Journal ArticleDOI
Methods of combining multiple classifiers and their applications to handwriting recognition
Lei Xu,Adam Krzyżak,Ching Y. Suen +2 more
- Vol. 22, Iss: 3, pp 418-435
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TLDR
On applying these methods to combine several classifiers for recognizing totally unconstrained handwritten numerals, the experimental results show that the performance of individual classifiers can be improved significantly.Abstract:
Possible solutions to the problem of combining classifiers can be divided into three categories according to the levels of information available from the various classifiers. Four approaches based on different methodologies are proposed for solving this problem. One is suitable for combining individual classifiers such as Bayesian, k-nearest-neighbor, and various distance classifiers. The other three could be used for combining any kind of individual classifiers. On applying these methods to combine several classifiers for recognizing totally unconstrained handwritten numerals, the experimental results show that the performance of individual classifiers can be improved significantly. For example, on the US zipcode database, 98.9% recognition with 0.90% substitution and 0.2% rejection can be obtained, as well as high reliability with 95% recognition, 0% substitution, and 5% rejection. >read more
Citations
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An improved fuzzy ARTMAP and Q-learning agent model for pattern classification
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Proceedings ArticleDOI
Minority Vote: At-Least-N Voting Improves Recall for Extracting Relations
TL;DR: A novel scheme for voting among a committee of classifiers that can significantly boost the recall in asymmetric NLP tasks where one class label NONE dominates all other classes.
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Support vector machine and generalized regression neural network based classification fusion models for cancer diagnosis
TL;DR: Experimental results show that the new proposed fusion methodology for selecting the best and removing weak classifiers outperforms single classification models.
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Aggregative model-based classifier ensemble for improving land-use/cover classification of Landsat TM Images
Xuecao Li,Xiaoping Liu,Le Yu +2 more
TL;DR: Experimental results show that the proposed model was significantly better than the most accurate single classification (i.e. SVM) in terms of classification accuracy and kappa coefficient.
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Chinese text location under complex background using Gabor filter and SVM
Jianqiang Yan,Jie Li,Xinbo Gao +2 more
TL;DR: This paper presents a novel method by combining Gabor filter and support vector machine (SVM) for Chinese text location, which achieves better results than some existing methods.
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