Presentation | 2003/12/12 Fault Tolerant Training of Neural Networks for Learning Vector Quantization Takashi MINOHARA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | The learning vector quantization(LVQ) is a model of neural networks, and it is used for complex pattern classifications in which typical feedforward networks don't give a good performance. Fault tolerance is an important feature in the neural networks, when they are used for critical application. Many methods for enhancing the fault tolerance of neural networks have been proposed, but most of them are for feedforward networks. There is scarcely any methods for fault tolerance of LVQ neural networks. In this paper, we proposed a dependability measure for the LVQ neural networks, and then we presented two idea, the border emphasis and the encouragement of coupling, to improve the learning algorithm for increasing dependability. The experiment result shows that the proposed algorithm trains networks so that they can achieve high dependability. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Neural Networks / Learning Vector Quantization / Dependability Measure / Border Emphasis / Encouragement of Coupling |
Paper # | DC2003-83 |
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Conference Information | |
Committee | DC |
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Conference Date | 2003/12/12(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
Topics (in English) | |
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Paper Information | |
Registration To | Dependable Computing (DC) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Fault Tolerant Training of Neural Networks for Learning Vector Quantization |
Sub Title (in English) | |
Keyword(1) | Neural Networks |
Keyword(2) | Learning Vector Quantization |
Keyword(3) | Dependability Measure |
Keyword(4) | Border Emphasis |
Keyword(5) | Encouragement of Coupling |
1st Author's Name | Takashi MINOHARA |
1st Author's Affiliation | Department of Computer Science, Takushoku University() |
Date | 2003/12/12 |
Paper # | DC2003-83 |
Volume (vol) | vol.103 |
Number (no) | 535 |
Page | pp.pp.- |
#Pages | 6 |
Date of Issue |