A New Simplified Gravitational Clustering Method for Multi-prototype Learning Based on Minimum Classification Error Training

机译：基于最小分类误差训练的多原型学习简化引力聚类新方法

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In this paper, we propose a new simplified gravitational clustering method for multi-prototype learning based on minimum classification error (MCE) training. It simulates the process of the attraction and merging of objects due to their gravity force. The procedure is simplified by not considering velocity and multi-force attraction. The proposed hierarchical method does not depend on random initialization and the results can be used as better initial centers for K-means to achieve higher performance under the SSE (sum-squared-error) criterion. The experimental results on the recognition of handwritten Chinese characters show that the proposed approach can generate better prototypes than K-means and the results obtained by MCE training can be further improved when the proposed method is employed.

机译：在本文中，我们提出了一种基于最小分类误差（MCE）训练的多原型学习的简化引力聚类新方法。它模拟了由于重力引起的物体吸引和融合的过程。通过不考虑速度和多力吸引力来简化该过程。所提出的分层方法不依赖于随机初始化，并且该结果可以用作K均值的更好的初始中心，以在SSE（和平方误差）标准下实现更高的性能。手写汉字识别的实验结果表明，与K-means相比，该方法可以生成更好的原型，并且采用MCE训练可以进一步提高结果。

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来源
《International Workshop on Intelligent Computing in Pattern Analysis/Synthesis(IWICPAS 2006); 20060826-27; Xi'an(CN)》|2006年|P.168-175|共8页
会议地点 Xian(CN)
作者
Teng Long; Lian-Wen Jin;
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作者单位

Department of Electronic and Communication Engineering, South China University of Technology, 510640 Guangzhou, China;

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原文格式 PDF
正文语种 eng
中图分类信息处理（信息加工）;
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A New Simplified Gravitational Clustering Method for Multi-prototype Learning Based on Minimum Classification Error Training

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