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Simple estimate of the width in Gaussian kernel with adaptive scaling technique
https://doi.org/10.24517/00008344
https://doi.org/10.24517/000083449864c61d-c21e-4887-b531-ddca844dc508
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
TE-PR-KITAYAMA-S-4726.pdf (664.5 kB)
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| Item type | 学術雑誌論文 / Journal Article(1) | |||||||||
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| 公開日 | 2017-10-03 | |||||||||
| タイトル | ||||||||||
| タイトル | Simple estimate of the width in Gaussian kernel with adaptive scaling technique | |||||||||
| 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 | |||||||||
| ID登録 | ||||||||||
| ID登録 | 10.24517/00008344 | |||||||||
| ID登録タイプ | JaLC | |||||||||
| 著者 |
Kitayama, Satoshi
× Kitayama, Satoshi× Yamazaki, Koetsu |
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| 著者別表示 |
北山, 哲士
× 北山, 哲士
× 山崎, 光悦
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| 書誌情報 |
Applied Soft Computing Journal 巻 11, 号 8, p. 4726-4737, 発行日 2011-12-01 |
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| ISSN | ||||||||||
| 収録物識別子タイプ | ISSN | |||||||||
| 収録物識別子 | 1568-4946 | |||||||||
| NCID | ||||||||||
| 収録物識別子タイプ | NCID | |||||||||
| 収録物識別子 | AA11926126 | |||||||||
| DOI | ||||||||||
| 関連タイプ | isVersionOf | |||||||||
| 識別子タイプ | DOI | |||||||||
| 関連識別子 | 10.1016/j.asoc.2011.07.011 | |||||||||
| 出版者 | ||||||||||
| 出版者 | Elsevier | |||||||||
| 抄録 | ||||||||||
| 内容記述タイプ | Abstract | |||||||||
| 内容記述 | This paper presents a simple method to estimate the width of Gaussian kernel based on an adaptive scaling technique. The Gaussian kernel is widely employed in radial basis function (RBF) network, support vector machine (SVM), least squares support vector machine (LS-SVM), Kriging models, and so on. It is widely known that the width of the Gaussian kernel in these machine learning techniques plays an important role. Determination of the optimal width is a time-consuming task. Therefore, it is preferable to determine the width with a simple manner. In this paper, we first examine a simple estimate of the width proposed by Nakayama et al. Through the examination, four sufficient conditions for the simple estimate of the width are described. Then, a new simple estimate for the width is proposed. In order to obtain the proposed estimate of the width, all dimensions are equally scaled. A simple technique called the adaptive scaling technique is also developed. It is expected that the proposed simple method to estimate the width is applicable to wide range of machine learning techniques employing the Gaussian kernel. Through examples, the validity of the proposed simple method to estimate the width is examined. © 2011 Elsevier B.V. All rights reserved. | |||||||||
| 著者版フラグ | ||||||||||
| 出版タイプ | AM | |||||||||
| 出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||||||
| 関連URI | ||||||||||
| 識別子タイプ | URI | |||||||||
| 関連識別子 | http://www.elsevier.com/locate/issn/15684969 | |||||||||
