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Titlebook: Machine Learning and Data Mining in Pattern Recognition; 10th International C Petra Perner Conference proceedings 2014 Springer Internation

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書(shū)目名稱(chēng)Machine Learning and Data Mining in Pattern Recognition
副標(biāo)題10th International C
編輯Petra Perner
視頻videohttp://file.papertrans.cn/621/620467/620467.mp4
叢書(shū)名稱(chēng)Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Machine Learning and Data Mining in Pattern Recognition; 10th International C Petra Perner Conference proceedings 2014 Springer Internation
描述This book constitutes the refereed proceedings of the 10th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2014, held in St. Petersburg, Russia in July 2014. The 40 full papers presented were carefully reviewed and selected from 128 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining.
出版日期Conference proceedings 2014
關(guān)鍵詞association rule mining; bayesian network; bioinformatics; crowdsourcing; data mining; ensemble method; ma
版次1
doihttps://doi.org/10.1007/978-3-319-08979-9
isbn_softcover978-3-319-08978-2
isbn_ebook978-3-319-08979-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing Switzerland 2014
The information of publication is updating

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Eran Shaham,David Sarne,Boaz Ben-Mosheasche und eines weiten Neusilbertrichters ohne Verlust in einen 100 ccm-Kolben, kl?rt nach Bedarf mit Bleiessig, d. h. bis nach dem Absetzen des entstehenden Niederschlages auf Zusatz einiger weiterer Tropfen keine Trübung mehr entsteht, und polarisirt im 200 mm-Rohr. Die abgelesenen Grade sind wege
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Towards the Efficient Recovery of General Multi-Dimensional Bayesian Network Classifierional Bayesian network classifier (MBNC) was devised for MDC in 2006, but with restricted structure. By removing the constraints, an undocumented model called general multi-dimensional Bayesian network classifier (GMBNC) is proposed in this article, along with an exact induction algorithm which is a
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Multiple Regression Method Based on Unexpandable and Irreducible Convex Combinationssearched with an ordinary least squares technique. Convex combination is considered optimal if it correlates with the response variable in the best way. It is shown that the developed approach is equivalent to a least squares technique variant regularized by constraints on signs of regression parame
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ACCD: Associative Classification over Concept-Drifting Data Streamstive classification over data streams. Different from data in traditional static databases, data streams typically arrive continuously and unboundedly with occasionally changing data distribution known as concept drift. In this paper, we propose a new Associative Classification over Concept-Drifting
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