| Presentation | 2007-01-17 Midpoint Validation Method of Neural Networks for Pattern Classification Problems Yasunori SAKAMOTO, Hiroki TAMURA, Koichi TANNO, Kazuya YAMASHITA, Masahiro ISHII, Masaaki TODO, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In this paper, we propose a midpoint validation method which improves the generalization of neural networks. The problem associated with the former cross-validation method is that efficiency is infected due to the separation of training data into two or N set. As for the proposed method, it creates new data from the known training data and computes a set of criteria using the newly created data and the previous training data. The implementation is easy since there is no unnecessary processing involved in separating the data into two or N sets. The advantage of the proposed method is that the method becomes much more efficient compared to the former method due to the numerical simulation used. We compare its performance with those of the Support Vector Machine (abbr. SVM), Multilayer Perceptron (abbr. MLP), Radial Basis Function (abbr. RBF) and proposed method on several benchmark problems. The results obtained from the simulation carried out shows the effectiveness of the proposed method. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Neural Network / Cross-Validation / Ionosphere data / Pima-indians-diabetes data / Wisconsin breast cancer data |
| Paper # | NLP2006-114 |
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| Conference Information | |
| Committee | NLP |
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| Conference Date | 2007/1/10(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
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| Paper Information | |
| Registration To | Nonlinear Problems (NLP) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Midpoint Validation Method of Neural Networks for Pattern Classification Problems |
| Sub Title (in English) | |
| Keyword(1) | Neural Network |
| Keyword(2) | Cross-Validation |
| Keyword(3) | Ionosphere data |
| Keyword(4) | Pima-indians-diabetes data |
| Keyword(5) | Wisconsin breast cancer data |
| 1st Author's Name | Yasunori SAKAMOTO |
| 1st Author's Affiliation | Faculty of Engineering, University of TOYAMA() |
| 2nd Author's Name | Hiroki TAMURA |
| 2nd Author's Affiliation | Faculty of Engineering, University of MIYAZAKI |
| 3rd Author's Name | Koichi TANNO |
| 3rd Author's Affiliation | Faculty of Engineering, University of MIYAZAKI |
| 4th Author's Name | Kazuya YAMASHITA |
| 4th Author's Affiliation | Faculty of Engineering, University of TOYAMA |
| 5th Author's Name | Masahiro ISHII |
| 5th Author's Affiliation | Faculty of Engineering, University of TOYAMA |
| 6th Author's Name | Masaaki TODO |
| 6th Author's Affiliation | Faculty of Engineering, University of TOYAMA |
| Date | 2007-01-17 |
| Paper # | NLP2006-114 |
| Volume (vol) | vol.106 |
| Number (no) | 451 |
| Page | pp.pp.- |
| #Pages | 6 |
| Date of Issue | |