支持向量机在手写数字分类中的应用

5th International Conference on Intelligent Systems Design and Applications (ISDA'05) Pub Date : 2005-09-08 DOI:10.1109/ISDA.2005.87

Urszula Markowska-Kaczmar, Pawel Kubacki

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引用次数: 10

摘要

本文描述了用支持向量机对手写数字进行分类的方法。由于支持向量机的训练时间长、不令人满意，我们提出采用曼哈顿距离的k近邻算法来减小训练集的大小，希望这种混合方法不会使识别结果明显变差。提出进一步实验的目的是验证这一假设。

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Support vector machines in handwritten digits classification

In the paper our approach to classify handwritten digits by using support vector machines is described. Because of the unsatisfying, long time of training of SVM we propose to apply k-nearest neighbours algorithm with Manhattan distance to obtain reduced size of training set having a hope that this hybrid method does not make the significantly worse results of recognition. The aim of presented further experiments was to verify this assumption.

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5th International Conference on Intelligent Systems Design and Applications (ISDA'05)

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