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CombNET-III with Nonlinear Gating Network and Its Application in Large-Scale Classification Problems
https://nitech.repo.nii.ac.jp/records/5371
https://nitech.repo.nii.ac.jp/records/537163c6c1ec-1441-4f05-95f4-16e4ebee847f
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
本文_fulltext (546.0 kB)
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Copyright(c)2008 IEICE http://search.ieice.org/index.html
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| Item type | 学術雑誌論文 / Journal Article(1) | |||||||||||||
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| 公開日 | 2012-11-07 | |||||||||||||
| タイトル | ||||||||||||||
| タイトル | CombNET-III with Nonlinear Gating Network and Its Application in Large-Scale Classification Problems | |||||||||||||
| 言語 | en | |||||||||||||
| 言語 | ||||||||||||||
| 言語 | eng | |||||||||||||
| 資源タイプ | ||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||
| 資源タイプ | journal article | |||||||||||||
| 著者 |
Kugler, Mauricio
× Kugler, Mauricio
× Kuroyanagi, Susumu
× Nugroho, Anto Satriyo
× Iwata, Akira
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| 著者別名 | ||||||||||||||
| 姓名 | 黒柳, 奨 | |||||||||||||
| 著者別名 | ||||||||||||||
| 姓名 | 岩田, 彰 | |||||||||||||
| bibliographic_information |
en : IEICE transactions on information and systems 巻 E91-D, 号 2, p. 286-295, 発行日 2008-02-01 |
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| 出版者 | ||||||||||||||
| 出版者 | Institute of Electronics, Information and Communication Engineers | |||||||||||||
| 言語 | en | |||||||||||||
| ISSN | ||||||||||||||
| 収録物識別子タイプ | ISSN | |||||||||||||
| 収録物識別子 | 0916-8532 | |||||||||||||
| item_10001_source_id_32 | ||||||||||||||
| 収録物識別子タイプ | NCID | |||||||||||||
| 収録物識別子 | AA10826272 | |||||||||||||
| 出版タイプ | ||||||||||||||
| 出版タイプ | VoR | |||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||
| 内容記述 | ||||||||||||||
| 内容記述タイプ | Other | |||||||||||||
| 内容記述 | Modern applications of pattern recognition generate very large amounts of data, which require large computational effort to process. However, the majority of the methods intended for large-scale problems aim to merely adapt standard classification methods without considering if those algorithms are appropriated for large-scale problems. CombNET-II was one of the first methods specifically proposed for such kind of a task. Recently, an extension of this model, named CombNET-III, was proposed. The main modifications over the previous model was the substitution of the expert networks by Support Vectors Machines (SVM) and the development of a general probabilistic framework. Although the previous model's performance and flexibility were improved, the low accuracy of the gating network was still compromising CombNET-III's classification results. In addition, due to the use of SVM based experts, the computational complexity is higher than CombNET-II. This paper proposes a new two-layered gating network structure that reduces the compromise between number of clusters and accuracy, increasing the model's performance with only a small complexity increase. This high-accuracy gating network also enables the removal the low confidence expert networks from the decoding procedure. This, in addition to a new faster strategy for calculating multiclass SVM outputs significantly reduced the computational complexity. Experimental results of problems with large number of categories show that the proposed model outperforms the original CombNET-III, while presenting a computational complexity more than one order of magnitude smaller. Moreover, when applied to a database with a large number of samples, it outperformed all compared methods, confirming the proposed model's flexibility. | |||||||||||||
| 言語 | en | |||||||||||||
