Adaptive tangent distance classifier on recognition of handwritten digits

Shuen-Lin Jeng, Yu Te Liu

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)

摘要

Simard et al. [16,17] proposed a transformation distance called "tangent distance" (TD) which can make pattern recognition be efficient. The key idea is to construct a distance measure which is invariant with respect to some chosen transformations. In this research, we provide a method using adaptive TD based on an idea inspired by "discriminant adaptive nearest neighbor" [7]. This method is relatively easy compared with many other complicated ones. A real handwritten recognition data set is used to illustrate our new method. Our results demonstrate that the proposed method gives lower classification error rates than those by standard implementation of neural networks and support vector machines and is as good as several other complicated approaches.

原文English
頁(從 - 到)2647-2659
頁數13
期刊Journal of Applied Statistics
38
發行號11
DOIs
出版狀態Published - 2011 十一月 1

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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