Presentation | 2002/12/6 A supervised learning rule using duality in neuron model Kiichi YAMADA, Susumu KUROYANAGI, Akira IWATA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Duality is a property that is found to be exists between weight vector and the input vector of an artificial neuron model. When the desired value of synaptic weight is able to be obtained by an algorithm, the duality property enable us to also calculate the desired value of the input, simultaneously. Furthermore, when the input to a neuron model is obtained from output of the other neuron to which it is connected, the desired input of the former neuron is identical to the desired output of the latter. As an example, we propose a learning rule for a network that have an additional output layer to an LVQ network. The simulation result of a classification problem showed that the proposed algorithm outperformed the LVQ, and the category allocation to the neuron could be performed dynamically. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | duality / neural network / learning rule / LVQ |
Paper # | NC2002-90 |
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Committee | NC |
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Conference Date | 2002/12/6(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Neurocomputing (NC) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | A supervised learning rule using duality in neuron model |
Sub Title (in English) | |
Keyword(1) | duality |
Keyword(2) | neural network |
Keyword(3) | learning rule |
Keyword(4) | LVQ |
1st Author's Name | Kiichi YAMADA |
1st Author's Affiliation | Dept. of Electrical and Computer Eng., Nagoya Institute of Technology() |
2nd Author's Name | Susumu KUROYANAGI |
2nd Author's Affiliation | Dept. of Electrical and Computer Eng., Nagoya Institute of Technology |
3rd Author's Name | Akira IWATA |
3rd Author's Affiliation | Dept. of Electrical and Computer Eng., Nagoya Institute of Technology |
Date | 2002/12/6 |
Paper # | NC2002-90 |
Volume (vol) | vol.102 |
Number (no) | 508 |
Page | pp.pp.- |
#Pages | 6 |
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