WEKO3
アイテム
Improving LogitBoost with Prior Knowledge.
http://hdl.handle.net/10445/6919
http://hdl.handle.net/10445/6919e60686ca-2fb6-460e-b475-10bd9013f107
| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2013-04-15 | |||||
| タイトル | ||||||
| タイトル | Improving LogitBoost with Prior Knowledge. | |||||
| 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 | |||||
| アクセス権 | ||||||
| アクセス権 | metadata only access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
| 著者 |
Kanamori, Takafumi
× Kanamori, Takafumi× 竹之内, 高志 |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | The purpose of this study is to incorporate prior knowledge into a boosting algorithm. Existing approaches require additional samples that represent the prior knowledge. Moreover, in order to adjust the balance between the information in training samples and the prior knowledge in the data domain, one needs to repeat the boosting algorithm with a different regularization parameter. These properties lead to costly computation. In this paper, we propose a boosting algorithm with prior knowledge that avoids computational issues. In our method, the mixture distribution of the estimator and prior knowledge is considered. We describe numerical experiments showing the effectiveness of our approach. | |||||
| 書誌情報 |
Information Fusion 発行日 2013-04-15 |
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| 査読有無 | ||||||
| 値 | あり/yes | |||||
| 研究業績種別 | ||||||
| 値 | 原著論文/Original Paper | |||||
| 単著共著 | ||||||
| 値 | 共著/joint | |||||
| 出版者 | ||||||
| 出版者 | Elsevier | |||||
