Presentation | 1995/11/24 Towards Practical Worst-Case Learning Curve Bounds Hanzhong Gu, Haruhisa Takahashi, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | We motivate the need for estimating bounds on learning curves of an average-case learning algorithm when it performs the worst on a training sample. The learning algorithm we use is a so-called ill-posed learning algorithm, which, when performing in average, is worse than the Gibbs learning algorithm, and when performing the worst, well models the worst-case in average-case learning processes. We simultaneously investigate learning curves when the algorithm performs in average, the best, and the worst. This leads us a new understanding of worst-case generalization in real learning situations, which differs significantly from that in the uniform learnable setting via the VC dimension analysis. We illustrate the results with some numerical simulations. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Generalization / Concept Learning / Learning Curves / Sample Complexity / PAC Learning |
Paper # | NC95-67 |
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Committee | NC |
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Conference Date | 1995/11/24(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Neurocomputing (NC) |
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Language | ENG |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Towards Practical Worst-Case Learning Curve Bounds |
Sub Title (in English) | |
Keyword(1) | Generalization |
Keyword(2) | Concept Learning |
Keyword(3) | Learning Curves |
Keyword(4) | Sample Complexity |
Keyword(5) | PAC Learning |
1st Author's Name | Hanzhong Gu |
1st Author's Affiliation | Department of Communications and Systems Engineering The University of Electro-Communications() |
2nd Author's Name | Haruhisa Takahashi |
2nd Author's Affiliation | Department of Communications and Systems Engineering The University of Electro-Communications |
Date | 1995/11/24 |
Paper # | NC95-67 |
Volume (vol) | vol.95 |
Number (no) | 389 |
Page | pp.pp.- |
#Pages | 8 |
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