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Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Wray Buntine,Marko Grobelnik,John Shawe-Taylor Conference proce

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書目名稱Machine Learning and Knowledge Discovery in Databases
副標(biāo)題European Conference,
編輯Wray Buntine,Marko Grobelnik,John Shawe-Taylor
視頻videohttp://file.papertrans.cn/621/620522/620522.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Wray Buntine,Marko Grobelnik,John Shawe-Taylor Conference proce
描述This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2009, held in Bled, Slovenia, in September 2009. The 106 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 422 paper 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 2009
關(guān)鍵詞classification; clustering; data mining; database; graph mining; information retrieval; knowledge discover
版次1
doihttps://doi.org/10.1007/978-3-642-04174-7
isbn_softcover978-3-642-04173-0
isbn_ebook978-3-642-04174-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2009
The information of publication is updating

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On Discriminative Parameter Learning of Bayesian Network Classifiersion and handwritten digit recognition. Discriminative parameter learning shows a significant improvement over generative ML estimation for naive Bayes (NB) and tree augmented naive Bayes (TAN) structures on all data sets. In general, the performance improvement of discriminative parameter learning i
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