| Presentation | 2005-12-16 Adaptive Subspace Splitting Using Minimum-Description-Length-Principles for Efficient Boosting Duy Dinh LE, Shinichi SATOH, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In RealBoost learning, selecting best weak classifiers is one of the most significant tasks. Generally, it is done according to its discriminant power which are normally measured by dissimilarity of distributions of positive and negative samples such as Bhattacharyya distance, Kullback-Leibler divergence, the recent Jensen-Shannon divergence and Informax. These distributions are often estimated through splitting the range of continuous feature values into a predefined number of equal-width intervals and then computing histograms. So far, choosing the most appropriate number of intervals is still a challenging task because a small number of intervals might not well approximate the real distribution while a large number of intervals might cause over-fitting, increase computation time and waste storage space. Therefore, this paper proposes using Minimum-Description-Length-Principles (MDLP) based discretization method for automatically and optimally choosing it. Experiments on the integrating MDLP-based subspace splitting into RealBoost have shown that strong classifiers learned by the proposed method can achieve stable performance, avoid over-fitting and have compact storage space. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | AdaBoost / MDLP based discretization / object detection |
| Paper # | PRMU2005-130 |
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| Conference Information | |
| Committee | PRMU |
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| Conference Date | 2005/12/9(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| 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) | Adaptive Subspace Splitting Using Minimum-Description-Length-Principles for Efficient Boosting |
| Sub Title (in English) | |
| Keyword(1) | AdaBoost |
| Keyword(2) | MDLP based discretization |
| Keyword(3) | object detection |
| 1st Author's Name | Duy Dinh LE |
| 1st Author's Affiliation | Department of Informatics, The Graduate University for Advanced Studies() |
| 2nd Author's Name | Shinichi SATOH |
| 2nd Author's Affiliation | Department of Informatics, The Graduate University for Advanced Studies:National Institute of Informatics |
| Date | 2005-12-16 |
| Paper # | PRMU2005-130 |
| Volume (vol) | vol.105 |
| Number (no) | 478 |
| Page | pp.pp.- |
| #Pages | 6 |
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