Presentation | 1998/3/20 SUPERLINEAR AND AUTOMATICALLY ADAPTABLE CONJUGATE GRADIENT TRAINING ALGORITHM Peter GECZY, Shiro USUI, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Conjugate gradient optimization methods (e. g. BP with momentum) are among the most used MLP network training techniques. Difficulty with the use of the methods is due to the necessity of finding the appropriate values of learning parameters (e. g. learning rate and momentum). This paper introduces a novel algorithm with automatically and dynamically adaptable learning parameters. Learning rate and momentum term are optimally determined at each iteration in a single-step calculation. The newly proposed algorithm has the same computational complexity as BP with momentum, however, it is convergent with superlinear convergence rates, i. e. the fastest convergence rates for first order techniques. Apart from the theoretical justification the simulation results indicate superior performance of the proposed algorithm over the standard BP with momentum term. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | first order optimization / conjugate gradient / line search subproblem / adjustable parameters |
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Committee | NC |
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Conference Date | 1998/3/20(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (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) | SUPERLINEAR AND AUTOMATICALLY ADAPTABLE CONJUGATE GRADIENT TRAINING ALGORITHM |
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Keyword(1) | first order optimization |
Keyword(2) | conjugate gradient |
Keyword(3) | line search subproblem |
Keyword(4) | adjustable parameters |
1st Author's Name | Peter GECZY |
1st Author's Affiliation | Department of Information and Computer Sciences, Toyohashi University of Technology() |
2nd Author's Name | Shiro USUI |
2nd Author's Affiliation | Department of Information and Computer Sciences, Toyohashi University of Technology |
Date | 1998/3/20 |
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Volume (vol) | vol.97 |
Number (no) | 624 |
Page | pp.pp.- |
#Pages | 8 |
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