An efficient algorithm for learning invariances in adaptive classifiers

P. Simard, Y. Le Cun, J. Denker, B. Victorri

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

Abstract

In many machine learning applications, one has not only training data but also some high-level information about certain invariances that the system should exhibit. In character recognition, for example, the answer should be invariant with respect to small spatial distortions in the input images (translations, rotations, scale changes, etcetera). We have implemented a scheme that minimizes the derivative of the classifier outputs with respect to distortion operators of our choosing. This not only produces tremendous speed advantages, but also provides a powerful language for specifying what generalizations we wish the network to perform.

Original languageEnglish (US)
Title of host publicationConference B
Subtitle of host publicationPattern Recognition Methodology and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages651-655
Number of pages5
ISBN (Print)0818629150
DOIs
StatePublished - 1992
Event11th IAPR International Conference on Pattern Recognition, IAPR 1992 - The Hague, Netherlands
Duration: Aug 30 1992Sep 3 1992

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2
ISSN (Print)1051-4651

Other

Other11th IAPR International Conference on Pattern Recognition, IAPR 1992
CountryNetherlands
CityThe Hague
Period8/30/929/3/92

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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