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Feature Extraction Based on Space Folding Model and Application to Machine Learning
http://hdl.handle.net/2237/20689
http://hdl.handle.net/2237/20689a112ac37-9823-44a1-a682-b680cd14249f
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
2010_322.pdf (1.4 MB)
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| Item type | 会議発表論文 / Conference Paper(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2014-11-05 | |||||
| タイトル | ||||||
| タイトル | Feature Extraction Based on Space Folding Model and Application to Machine Learning | |||||
| 言語 | en | |||||
| 著者 |
Minh, Tuan Pham
× Minh, Tuan Pham× Tachibana, Kanta× Yoshikawa, Tomohiro× Furuhashi, Takeshi |
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| アクセス権 | ||||||
| アクセス権 | open access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Classification | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Cross Entropy | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Space Folding Vector | |||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | One of the most important designs for a lot of machine learning methods is the determination of the similarity between instances. Especially the kernel matrix, which is also known as the Gram matrix, plays a central role in the kernel machines such as support vector machine. The simplest design of similarity function is to use the distances between instances or the Gaussian function based on them. It is easy to learn the model when the data distribution follows their label, in which the instances with same label are allocated near and those with different label are allocated far. However, when the data distribution is non-linear, it becomes difficult. This paper discusses the inner products of 2 non-orthogonal basis vectors and proposes the similarity between instances. This paper also proposes a space folding model for machine learning based on the proposed similarity. This paper applies the proposed method to pattern recognition problem and shows its effectiveness. | |||||
| 言語 | en | |||||
| 内容記述 | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | SCIS & ISIS 2010, Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems. December 8-12, 2010, Okayama Convention Center, Okayama, Japan | |||||
| 言語 | en | |||||
| 内容記述 | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | Session ID: TH-F3-4 | |||||
| 言語 | en | |||||
| 出版者 | ||||||
| 出版者 | 日本知能情報ファジィ学会 | |||||
| 言語 | ja | |||||
| 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_5794 | |||||
| 資源タイプ | conference paper | |||||
| 出版タイプ | ||||||
| 出版タイプ | VoR | |||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
| DOI | ||||||
| 関連タイプ | isVersionOf | |||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | https://doi.org/10.14864/softscis.2010.0.322.0 | |||||
| 書誌情報 |
en : SCIS & ISIS 巻 2010, p. 322-327, 発行日 2010 |
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| 著者版フラグ | ||||||
| 値 | publisher | |||||
| URI | ||||||
| 識別子 | http://dx.doi.org/10.14864/softscis.2010.0.322.0 | |||||
| 識別子タイプ | DOI | |||||
| URI | ||||||
| 識別子 | http://hdl.handle.net/2237/20689 | |||||
| 識別子タイプ | HDL | |||||
