Evaluation of prototype learning algorithms for nearest-neighbor classifier in application to handwritten character recognition
Evaluation of prototype learning algorithms for nearest-neighbor classifier in application to handwritten character recognition
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DOI：
10.1016/s0031-3203(00)00018-2
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发表时间：
2001-03
期刊：
影响因子：
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通讯作者：
Cheng-Lin Liu;M. Nakagawa
中科院分区：
文献类型：
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作者：
Cheng-Lin Liu;M. Nakagawa
原型学习在提高最近邻分类器的分类性能和减少存储和计算需求方面是有效的。本文综述了一些用于神经网络分类器设计的原型学习算法，并评价了它们在手写体字符识别中的应用性能。这些算法包括众所周知的LVQ算法和一些通过梯度搜索最小化目标函数的参数优化方法。我们还提出了一些基于参数优化的新算法，并与现有算法进行了性能评价。在CENPARMI数据库的手写体数字识别和ETL8B2数据库的手写体汉字识别中，对11种原型学习算法进行了测试。实验结果表明，基于参数优化的算法总体上优于LVQ算法。特别是Juang和Katagiri （IEEE Trans.）的最小分类误差（MCE）方法。信号处理，40 (12)(1992)3043)，Sato和Yamada的广义LVQ (GLVQ)（第14届ICPR论文集，Vol. I, Brisbane, 1998, p. 322）和一个新的算法MAXP1得到了最好的结果。
Prototype learning is effective in improving the classification performance of nearest-neighbor (NN) classifier and in reducing the storage and computation requirements. This paper reviews some prototype learning algorithms for NN classifier design and evaluates their performance in application to handwritten character recognition. The algorithms include the well-known LVQ and some parameter optimization approaches that aim to minimize an objective function by gradient search. We also propose some new algorithms based on parameter optimization and evaluate their performance together with the existing ones. Eleven prototype learning algorithms are tested in handwritten numeral recognition on the CENPARMI database and in handwritten Chinese character recognition on the ETL8B2 database. The experimental results show that the algorithms based on parameter optimization generally outperform the LVQ. Particularly, the minimum classification error (MCE) approach of Juang and Katagiri (IEEE Trans. Signal Process. 40 (12) (1992) 3043), the generalized LVQ (GLVQ) of Sato and Yamada (Proceedings of the 14th ICPR, Vol. I, Brisbane, 1998, p. 322) and a new algorithm MAXP1 yield best results.