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Titlebook: Machine Learning and Data Mining in Pattern Recognition; 8th International Co Petra Perner Conference proceedings 2012 Springer-Verlag Berl

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书目名称Machine Learning and Data Mining in Pattern Recognition
副标题8th International Co
编辑Petra Perner
视频video
概述Fast conference proceedings State of the art report Up to date results
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Machine Learning and Data Mining in Pattern Recognition; 8th International Co Petra Perner Conference proceedings 2012 Springer-Verlag Berl
描述This book constitutes the refereed proceedings of the 8th International Conference, MLDM 2012, held in Berlin, Germany in July 2012. The 51 revised full papers presented were carefully reviewed and selected from 212 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 2012
关键词data stream clustering; multi-relational classification; particle swarm optimization; support vector ma
版次1
doihttps://doi.org/10.1007/978-3-642-31537-4
isbn_softcover978-3-642-31536-7
isbn_ebook978-3-642-31537-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2012
The information of publication is updating

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Top-, Minimization Approach for Indicative Correlation Change Miningwo databases. As there exist many potential solutions, we apply top . control that attains the bottom . correlation values at the base for all the patterns satisfying the constraint..As we measure the degree of correlation by k-way mutual information, that is monotonically increasing with respect to
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Comparing Logistic Regression, Neural Networks, C5.0 and M5′ Classification Techniquesr than logistic regression over 2 data sets, equivalent in performance over 2 data sets and has low performance than logistic regression in case of 1 data set. It is observed that M5′ is a better classification technique than other techniques over 1 dataset.
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