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JAIST Repository >
科学技術開発戦略センター 2003～2008 >
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KSS'2007 >
このアイテムの引用には次の識別子を使用してください:
http://hdl.handle.net/10119/4111
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| タイトル: | Discovering Comprehensible Knowledge from Data using Ensemble Learning Techniques |
| 著者: | Zhou, Zhi-Hua |
| 発行日: | Nov-2007 |
| 出版者: | JAIST Press |
| 抄録: | Machine learning focuses on the study of algorithms that can improve the performance of computer programs with experiences, which has become one of the most important sources of intelligent data analysis techniques. Ensemble learning is a powerful machine learning paradigm. In contrast to traditional machine learning techniques which train one learner from data, ensemble learning techniques generate multiple or many learners from data to solve a problem. This talk will introduce some advances in the research of ensemble learning. In particular, the talk will show some methods for discovering comprehensible knowledge from data using ensemble learning techniques, which exhibits that ensemble learning techniques can be useful tools for the research of knowledge science. |
| 記述: | The original publication is available at JAIST Press http://www.jaist.ac.jp/library/jaist-press/index.html
Proceedings of KSS'2007 : The Eighth International Symposium on Knowledge and Systems Sciences : November 5-7, 2007, [Ishikawa High-Tech Conference Center, Nomi, Ishikawa, JAPAN]
Organized by: Japan Advanced Institute of Science and Technology |
| 言語: | ENG |
| URI: | http://hdl.handle.net/10119/4111 |
| ISBN: | 9784903092072 |
| 出現コレクション: | KSS'2007
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このアイテムのファイル:
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記述 |
サイズ | 形式 |
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| 85.pdf | | 7Kb | Adobe PDF | 見る/開く |
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当システムに保管されているアイテムはすべて著作権により保護されています。
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