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Titlebook: Machine Learning and Data Mining in Pattern Recognition; 7th International Co Petra Perner Conference proceedings 2011 Springer-Verlag GmbH

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發(fā)表于 2025-3-21 18:54:07 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Machine Learning and Data Mining in Pattern Recognition
副標(biāo)題7th International Co
編輯Petra Perner
視頻videohttp://file.papertrans.cn/621/620457/620457.mp4
概述Fast-track conference proceedings.State-of-the-art research.Up-to-date results
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Machine Learning and Data Mining in Pattern Recognition; 7th International Co Petra Perner Conference proceedings 2011 Springer-Verlag GmbH
描述This book constitutes the refereed proceedings of the 7th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2011, held in New York, NY, USA.The 44 revised full papers presented were carefully reviewed and selected from 170 submissions. The papers are organized in topical sections on classification and decision theory, theory of learning, clustering, application in medicine, webmining and information mining; and machine learning and image mining.
出版日期Conference proceedings 2011
關(guān)鍵詞algorithmic learning; data analysis; kernel methods; suppor vector machine; text analysis
版次1
doihttps://doi.org/10.1007/978-3-642-23199-5
isbn_softcover978-3-642-23198-8
isbn_ebook978-3-642-23199-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag GmbH Berlin Heidelberg 2011
The information of publication is updating

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沙發(fā)
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978-3-642-23198-8Springer-Verlag GmbH Berlin Heidelberg 2011
板凳
發(fā)表于 2025-3-22 03:46:15 | 只看該作者
Machine Learning and Data Mining in Pattern Recognition978-3-642-23199-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
地板
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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/m/image/620457.jpg
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https://doi.org/10.1007/978-3-642-23199-5algorithmic learning; data analysis; kernel methods; suppor vector machine; text analysis
7#
發(fā)表于 2025-3-22 19:27:41 | 只看該作者
Separability of Split Value Criterion with Weighted Separation Gainsed and discussed in comparison with the most popular decision tree node splitting criteria like information gain and Gini index. Because the new SSV definition introduces a parameter, some empirical analysis of the new parameter is presented. The new criterion turned out to be very successful in decision tree induction processes.
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Conference proceedings 201111, held in New York, NY, USA.The 44 revised full papers presented were carefully reviewed and selected from 170 submissions. The papers are organized in topical sections on classification and decision theory, theory of learning, clustering, application in medicine, webmining and information mining;
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