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Self-Organizing Map with Dynamical Node Splitting: Application to Handwritten Digit Recognition*

机译:具有动态节点拆分功能的自组织地图:应用于手写数字识别*

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摘要

This article presents a simple yet elegant pattern recognizer based on a dynamic node-splitting scheme for the self-organizing map that can adapt its structure as well as its weights. The scheme makes use of a structure adaptation capability to place the nodes of prototype vectors into the pattern space accurately so as to make the decision boundaries as close to the class boundaries as possible. In order to show the performance of the proposed scheme, experiments with the unconstrained handwritten digit database of Concordia University in Canada were conducted. The proposed method for an incremental formation of feature maps is 96.05 percent of the recognition rate. In view of the elegant simplicity of the approach, the reported performance is remarkable and can stand up to one of the best results reported in the literature with the same database.
机译:本文提出了一种基于动态节点拆分方案的简单而优雅的模式识别器,用于自组织图,该图可以适应其结构以及权重。该方案利用结构自适应能力将原型向量的节点准确地放置到模式空间中,从而使决策边界尽可能接近类边界。为了显示该方案的性能,在加拿大康考迪亚大学的无约束手写数字数据库上进行了实验。所提出的特征图增量形成方法为识别率的96.05%。鉴于该方法的简洁性,所报告的性能非常出色,并且在使用相同数据库的情况下,可以达到文献中所报告的最佳结果之一。

著录项

  • 来源
    《Neural computation》 |1997年第6期|1345-1355|共11页
  • 作者

    Cho S;

  • 作者单位

    Department of Computer Science, Yonsei University, Seoul, 120-749 Korea;

  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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