Mirror image learning for autoassociative neural networks

Shusaku Shimizu, Wataru Oyama, Tetsushi Wakabayashi, Fumitaka Kimura

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

1 Citation (Scopus)

Abstract

This paper studies on the mirror image learning algorithm for the autoassociative neural networks and evaluates the performance by handwritten numeral recognition test. Each of the autoassociative networks is first trained independently for each class using the feature vector of the class. Then the mirror image learning algorithm is applied to enlarge the learning sample of each class by mirror image patterns of the confusing classes to achieve higher recognition accuracy. Recognition accuracy of the autoassociative neural network classifier was improved by the mirror image learning from 98.76%to 99.23%in the recognition test for handwritten numeral database IPTP CD-ROM1 [1].

Original languageEnglish
Title of host publicationProceedings - 7th International Conference on Document Analysis and Recognition, ICDAR 2003
PublisherIEEE Computer Society
Pages804-808
Number of pages5
ISBN (Electronic)0769519601
DOIs
Publication statusPublished - Jan 1 2003
Event7th International Conference on Document Analysis and Recognition, ICDAR 2003 - Edinburgh, United Kingdom
Duration: Aug 3 2003Aug 6 2003

Publication series

NameProceedings of the International Conference on Document Analysis and Recognition, ICDAR
Volume2003-January
ISSN (Print)1520-5363

Other

Other7th International Conference on Document Analysis and Recognition, ICDAR 2003
CountryUnited Kingdom
CityEdinburgh
Period8/3/038/6/03

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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