Presentation 1997/2/7
A study of figure recognition process of a three-layered neural network learned geometrical feature space
Yasunori KATOH, Toshiaki TAKEDA, Takahide MATSUOKA,
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Abstract(in English) Using supervised learning, an input-output relation corresponding to feature extraction in 2 dimensional figures could be obtained in a 3 layered neural network. To analyze the mechanism for feature extraction in this network, we studied the reverse projection map of connection weights(RPMCW) from an output neuron upon the input layer. Each PRMCW showed excitatory area coinciding with the shape of feature assigned to the neuron. Strong inhibitory area was found at the position where a segment of different feature lies. Because the feature assigned to the output neuron shares segments with the other features, inhibitory area cffectively rejects the features which have overlapping segment similarly in the lateral inhibition in the brain.
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Keyword(in English) neural network / feature extraction / supervised learning / excitatory area / inhibitory area / lateral inhibition
Paper # NLP96-155,NC96-109
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Committee NC
Conference Date 1997/2/7(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A study of figure recognition process of a three-layered neural network learned geometrical feature space
Sub Title (in English)
Keyword(1) neural network
Keyword(2) feature extraction
Keyword(3) supervised learning
Keyword(4) excitatory area
Keyword(5) inhibitory area
Keyword(6) lateral inhibition
1st Author's Name Yasunori KATOH
1st Author's Affiliation Utsunomiya Univasity()
2nd Author's Name Toshiaki TAKEDA
2nd Author's Affiliation Jichi Mdical School, School of Nursing
3rd Author's Name Takahide MATSUOKA
3rd Author's Affiliation Utsunomiya Univasity
Date 1997/2/7
Paper # NLP96-155,NC96-109
Volume (vol) vol.96
Number (no) 512
Page pp.pp.-
#Pages 8
Date of Issue