| Presentation | 1996/2/3 A Constructive Learning Algorithm for HME Kazumi Saito, Ryohei Nakano, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | A Hierarchical Mixtures of Experts (HME) model has been applied to several classes of problems, and its usefulness has been shown. However, defining an adequate structure in advance is required and the resulting performance depends on the structure. To overcome this problem, a constructive learning algorithm for an HME is proposed; it includes an initialization method, a training method and an extension method. In our experiments, which used parity problems and a function approximation problem, the proposed algorithm worked much better than the conventional method. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | HME / constructive algorithm / initialization method / quasi-Newton's method |
| Paper # | NC95-114 |
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| Conference Information | |
| Committee | NC |
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| Conference Date | 1996/2/3(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | |
| Vice Chair | |
| Secretary | |
| Assistant | |
| Paper Information | |
| 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 Constructive Learning Algorithm for HME |
| Sub Title (in English) | |
| Keyword(1) | HME |
| Keyword(2) | constructive algorithm |
| Keyword(3) | initialization method |
| Keyword(4) | quasi-Newton's method |
| 1st Author's Name | Kazumi Saito |
| 1st Author's Affiliation | NTT Communication Science Laboratories() |
| 2nd Author's Name | Ryohei Nakano |
| 2nd Author's Affiliation | NTT Communication Science Laboratories |
| Date | 1996/2/3 |
| Paper # | NC95-114 |
| Volume (vol) | vol.95 |
| Number (no) | 506 |
| Page | pp.pp.- |
| #Pages | 8 |
| Date of Issue | |