Sciweavers

Share
ICPR
2002
IEEE

Comparative Study on Mirror Image Learning (MIL) and GLVQ

13 years 2 months ago
Comparative Study on Mirror Image Learning (MIL) and GLVQ
In this paper the effectiveness of a corrective learning algorithm MIL (Mirror Image Learning) [1], [2] is comparatively studied with that of GLVQ (Generalized Learning Vector Quantization) [3]. Both MIL and GLVQ were proposed to improve the learning effectiveness beyond the limitation due to independent estimation of class conditional distributions. While the GLVQ modifies the representative vectors of a pair of confusing classes when recognizing each learning pattern, the MIL generates a mirror image of a pattern which belongs to one of a pair of confusing classes and increases the size of the learning sample to update the distribution parameters. The performance of two algorithms is evaluated on handwritten numeral recognition test for IPTP CDROM1 [4]. Experimental results show that the recognition rate of projection distance classifier is improved from 99.31% to 99.40% by GLVQ and to 99.50% by MIL, respectively.
Meng Shi, Tetsushi Wakabayashi, Wataru Ohyama, Fum
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2002
Where ICPR
Authors Meng Shi, Tetsushi Wakabayashi, Wataru Ohyama, Fumitaka Kimura
Comments (0)
books