A comparative study on mirror image learning and ALSM

Tetsushi Wakabayashi, Meng Shi, Wataru Oyama, Fumitaka Kimura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, the effectiveness of a corrective learning algorithm MIL (mirror image learning) is comparatively studied with that of ALSM (average learning subspace method). Both MIL and ALSM were proposed to improve the learning effectiveness of class conditional distributions. While the ALSM modifies the basis vectors of a subspace by subtracting the autocorrelation matrix for counter classes from the one of its own class, the MIL generates a mirror image of a pattern which belongs to one of a pair of confusing classes to increases the size of the learning sample of the other class. The performance of two algorithms is evaluated on handwritten numeral recognition test for IPTP CDROMI. Experimental results show that the recognition rate of the subspace method is improved from 99.05% to 99.37% by ALSM and to 99.39% by MIL, respectively. Furthermore, the recognition rate of the projection distance method is improved from 99.13% to 99.35% by ALSM and to 99.44% by MIL.

Original languageEnglish
Title of host publicationProceedings - 8th International Workshop on Frontiers in Handwriting Recognition, IWFHR 2002
Pages151-156
Number of pages6
DOIs
Publication statusPublished - Dec 1 2002
Event8th International Workshop on Frontiers in Handwriting Recognition, IWFHR 2002 - Ontario, ON, Canada
Duration: Aug 6 2002Aug 8 2002

Publication series

NameProceedings - International Workshop on Frontiers in Handwriting Recognition, IWFHR
ISSN (Print)1550-5235

Other

Other8th International Workshop on Frontiers in Handwriting Recognition, IWFHR 2002
CountryCanada
CityOntario, ON
Period8/6/028/8/02

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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