NDLI: The effect of the inhibition-compensation learning scheme on n-tuple based classifier performance

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Object recognition supported by user interaction for service robots

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The effect of the inhibition-compensation learning scheme on n-tuple based classifier performance

Content Provider	IEEE Xplore Digital Library
Author	Hoque, S. Fairhurst, M.C. Guest, R.M.
Copyright Year	2002
Description	Author affiliation: Dept. of Electron., Kent Univ., Canterbury, UK (Hoque, S.; Fairhurst, M.C.; Guest, R.M.)
Abstract	The inhibition-compensation learning scheme (ICLS) has been proposed as a way of enhancing the performance of the moving window classifier. In the paper the effect of ICLS on three n-tuple based classification techniques has been investigated. Pre-segmented handwritten characters from the NIST database have been used as the pattern data. Results show that approximately 2-6% gain in classification accuracy can be achieved in the OCR task domain with no adverse effect on the classification throughput.
Starting Page	452
Ending Page	455
File Size	261377
Page Count	4
File Format	PDF
ISBN	076951695X
ISSN	10514651
DOI	10.1109/ICPR.2002.1047974
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-08-11
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Frequency Optical character recognition software Face recognition Speech recognition Chemical industry Testing NIST Image databases Image recognition Pattern recognition
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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