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Titlebook: Discovery Science; 10th International C Vincent Corruble,Masayuki Takeda,Einoshin Suzuki Conference proceedings 2007 Springer-Verlag Berlin

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书目名称Discovery Science
副标题10th International C
编辑Vincent Corruble,Masayuki Takeda,Einoshin Suzuki
视频videohttp://file.papertrans.cn/282/281058/281058.mp4
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Discovery Science; 10th International C Vincent Corruble,Masayuki Takeda,Einoshin Suzuki Conference proceedings 2007 Springer-Verlag Berlin
描述This volume contains the papers presented at DS-2007:The Tenth International Conference on Discovery Science held in Sendai, Japan, October 1–4, 2007. The main objective of the Discovery Science (DS) conference series is to p- vide an open forum for intensive discussions and the exchange of new ideas and information among researchers working in the area of automating scienti?c d- covery or working on tools for supporting the human process of discovery in science. It has been a successful arrangement in the past to co-locate the DS conference with the International Conference on Algorithmic Learning Theory (ALT). ThiscombinationofALTandDSallowsforacomprehensivetreatmentof the whole range, from theoretical investigations to practical applications. C- tinuing this tradition, DS 2007 was co-located with the 18th ALT conference (ALT 2007). The proceedings of ALT 2007 were published as a twin volume 4754 of the LNCS series. The International Steering Committee of the Discovery Science conference series provided important advice on a number of issues during the planning of Discovery Science 2007. The members of the Steering Committee are Einoshin Suzuki (Kyushu University, Chair), Achim G
出版日期Conference proceedings 2007
关键词Information Retrieval; algorithms; classification; clustering; data mining; discovery science; forecasting
版次1
doihttps://doi.org/10.1007/978-3-540-75488-6
isbn_softcover978-3-540-75487-9
isbn_ebook978-3-540-75488-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2007
The information of publication is updating

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Discovery Science978-3-540-75488-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
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A Hilbert Space Embedding for DistributionsWhile kernel methods are the basis of many popular techniques in supervised learning, they are less commonly used in testing, estimation, and analysis of probability distributions, where information theoretic approaches rule the roost. However it becomes difficult to estimate mutual information or entropy if the data are high dimensional.
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Studies in Indian Mathematics and Astronomy The project “Cyber Infrastructure for the Information-explosion Era” is a six-year project from 2005 to 2010 supported by Grant-in-Aid for Scientific Research on Priority Areas from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan. The project aims to establish the
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Family Influences on A. E. Garrod’s Thinkingtation, and scaling. While Kuramochi and Karypis (ICDM2002) extensively studied the frequent pattern mining problem for geometric subgraphs, the maximal graph mining has not been considered so far. In this paper, we study the maximal (or closed) graph mining problem for the general class of geometri
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