Presentation 1997/3/19
Handwritten Numeral Recognition using Competing Autoassociative Neural Networks
Satoshi INOUE, Tetsushi WAKABAYASHI, Shinji TSURUOKA, Fumitaka KIMURA, Yasuji MIYAKE,
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Abstract(in English) The autoassociative neural network is the one having the same number of neurons in input and output layers,and the less in the hidden layers.The network is trained using the input vector itself as the desired output.The autoassociative neural network has been applied to image compression and dimensionality reduction,and its relationship to the K-L expansion(Principal component analysis)has been studied.This paper proposes and investigates a new pattern classification technique using multiple competing autoassociative neural networks.
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Keyword(in English) neural network / autoassociative neural network / handwritten numeral recognition / K-L expansion / principal component analysis / projection distance
Paper # PRMU96-207
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Conference Information
Committee PRMU
Conference Date 1997/3/19(1days)
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Paper Information
Registration To Pattern Recognition and Media Understanding (PRMU)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Handwritten Numeral Recognition using Competing Autoassociative Neural Networks
Sub Title (in English)
Keyword(1) neural network
Keyword(2) autoassociative neural network
Keyword(3) handwritten numeral recognition
Keyword(4) K-L expansion
Keyword(5) principal component analysis
Keyword(6) projection distance
1st Author's Name Satoshi INOUE
1st Author's Affiliation Faculty of Engineering, Mie University()
2nd Author's Name Tetsushi WAKABAYASHI
2nd Author's Affiliation Faculty of Engineering, Mie University
3rd Author's Name Shinji TSURUOKA
3rd Author's Affiliation Faculty of Engineering, Mie University
4th Author's Name Fumitaka KIMURA
4th Author's Affiliation Faculty of Engineering, Mie University
5th Author's Name Yasuji MIYAKE
5th Author's Affiliation Faculty of Engineering, Mie University
Date 1997/3/19
Paper # PRMU96-207
Volume (vol) vol.96
Number (no) 599
Page pp.pp.-
#Pages 6
Date of Issue