Presentation | 2004/11/12 A New Method for Efficient Design of Neural Network Trees Qiangfu ZHAO, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Neural network tree (NNTree) is a hybrid learning model with the overall structure being a decision tree (DT), and each non-terminal node containing an expert neural network (ENN). Generally speaking, NNTrees outperform conventional DTs because more complex and possibly better features can be extracted by the ENNs. So far we have studied several genetic algorithms (GAs) for designing the NNTrees. These algorithms, however, are computationally expensive, and cannot be used easily. In this paper, we propose a new approach based on the R^4-rule, which is a non-genetic evolutionary algorithm proposed by the author several years ago. The key point is to propose a heuristic method for defining the teacher signals for the examples assigned to a non-terminal node. Once the teacher signals are defined, the ENNs can be trained quickly using the R^4-rule. Experiments with several public databases show that the new approach can produce NNTrees quickly and effectively. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Neural network / decision tree / neural network tree / nearest neighbor classifier / R^4-rule |
Paper # | PRMU2004-115,HIP2004-55 |
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Committee | PRMU |
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Conference Date | 2004/11/12(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Registration To | Pattern Recognition and Media Understanding (PRMU) |
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Language | ENG |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | A New Method for Efficient Design of Neural Network Trees |
Sub Title (in English) | |
Keyword(1) | Neural network |
Keyword(2) | decision tree |
Keyword(3) | neural network tree |
Keyword(4) | nearest neighbor classifier |
Keyword(5) | R^4-rule |
1st Author's Name | Qiangfu ZHAO |
1st Author's Affiliation | The University of Aizu() |
Date | 2004/11/12 |
Paper # | PRMU2004-115,HIP2004-55 |
Volume (vol) | vol.104 |
Number (no) | 448 |
Page | pp.pp.- |
#Pages | 6 |
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