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  1. B200 工学部/工学研究科
  2. B200e 会議資料
  3. 国際会議

Feature Extraction Based on Space Folding Model and Application to Machine Learning

http://hdl.handle.net/2237/20689
a112ac37-9823-44a1-a682-b680cd14249f
名前 / ファイル ライセンス アクション
2010_322.pdf 2010_322.pdf (1.4 MB)
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

WEKO 54026

Minh, Tuan Pham

Search repository
Tachibana, Kanta

× Tachibana, Kanta

WEKO 54027

Tachibana, Kanta

Search repository
Yoshikawa, Tomohiro

× Yoshikawa, Tomohiro

WEKO 54028

Yoshikawa, Tomohiro

Search repository
Furuhashi, Takeshi

× Furuhashi, Takeshi

WEKO 54029

Furuhashi, Takeshi

Search repository
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 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.
内容記述タイプ Abstract
内容記述
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
内容記述タイプ Other
内容記述
内容記述 Session ID: TH-F3-4
内容記述タイプ Other
出版者
出版者 日本知能情報ファジィ学会
言語
言語 eng
資源タイプ
資源 http://purl.org/coar/resource_type/c_5794
タイプ conference paper
書誌情報 SCIS & ISIS

巻 2010, p. 322-327, 発行日 2010
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
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