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Adaptive tangent distance classifier on recognition of handwritten digits

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  • Shuen-Lin Jeng
  • Yu-Te Liu

Abstract

Simard et al. [1617] 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.

Suggested Citation

  • Shuen-Lin Jeng & Yu-Te Liu, 2011. "Adaptive tangent distance classifier on recognition of handwritten digits," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(11), pages 2647-2659, February.
    • Handle: RePEc:taf:japsta:v:38:y:2011:i:11:p:2647-2659
    DOI: 10.1080/02664763.2011.567247
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