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Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Walter Daelemans,Bart Goethals,Katharina Morik Conference proce

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书目名称Machine Learning and Knowledge Discovery in Databases
副标题European Conference,
编辑Walter Daelemans,Bart Goethals,Katharina Morik
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Walter Daelemans,Bart Goethals,Katharina Morik Conference proce
描述This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008. The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.
出版日期Conference proceedings 2008
关键词Bayesian network; Boosting; Support Vector Machine; Web mining; active learning; algorithmic learning; ass
版次1
doihttps://doi.org/10.1007/978-3-540-87481-2
isbn_softcover978-3-540-87480-5
isbn_ebook978-3-540-87481-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2008
The information of publication is updating

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/m/image/620517.jpg
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Exceptional Model Miningsomehow exceptional. We discuss regression as well as classification models, and define quality measures that determine how exceptional a given model on a subgroup is. Our framework is general enough to be applied to many types of models, even from other paradigms such as association analysis and graphical modeling.
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A Joint Topic and Perspective Model for Ideological Discourse To cope with the non-conjugacy of the logistic-normal prior we derive a variational inference algorithm for the model. We evaluate the proposed model on synthetic data as well as a written and a spoken political discourse. Experimental results strongly support that ideological perspectives are reflected in lexical variations.
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Fitted Natural Actor-Critic: A New Algorithm for Continuous State-Action MDPs-spaces; in turn, the use of a regression-based critic allows for efficient use of data and avoids convergence problems that TD-based critics often exhibit. We establish the convergence of our algorithm and illustrate its application in a simple continuous space, continuous action problem.
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