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Titlebook: Machine Learning: ECML 2007; 18th European Confer Joost N. Kok,Jacek Koronacki,Andrzej Skowron Conference proceedings 2007 Springer-Verlag

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書(shū)目名稱Machine Learning: ECML 2007
副標(biāo)題18th European Confer
編輯Joost N. Kok,Jacek Koronacki,Andrzej Skowron
視頻videohttp://file.papertrans.cn/621/620755/620755.mp4
叢書(shū)名稱Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Machine Learning: ECML 2007; 18th European Confer Joost N. Kok,Jacek Koronacki,Andrzej Skowron Conference proceedings 2007 Springer-Verlag
描述The two premier annual European conferences in the areas of machine learning and data mining have been collocated ever since the ?rst joint conference in Freiburg, 2001. The European Conference on Machine Learning (ECML) traces its origins to 1986, when the ?rst European Working Session on Learning was held in Orsay, France. The European Conference on Principles and Practice of KnowledgeDiscoveryinDatabases(PKDD) was?rstheldin1997inTrondheim, Norway. Over the years, the ECML/PKDD series has evolved into one of the largest and most selective international conferences in machine learning and data mining. In 2007, the seventh collocated ECML/PKDD took place during September 17–21 on the centralcampus of WarsawUniversityand in the nearby Staszic Palace of the Polish Academy of Sciences. The conference for the third time used a hierarchical reviewing process. We nominated 30 Area Chairs, each of them responsible for one sub-?eld or several closely related research topics. Suitable areas were selected on the basis of the submission statistics for ECML/PKDD 2006 and for last year’s International Conference on Machine Learning (ICML 2006) to ensure a proper load balance amongtheAreaChairs.
出版日期Conference proceedings 2007
關(guān)鍵詞active learning; algorithmic learning; algorithms; classifier systems; cognition; genetic programming; ind
版次1
doihttps://doi.org/10.1007/978-3-540-74958-5
isbn_softcover978-3-540-74957-8
isbn_ebook978-3-540-74958-5Series 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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Neighborhood-Based Local Sensitivitycal sensitivity. The resulting estimates demonstrate improved performance when used in classifier combination and classifier recalibration as well as being potentially useful in active learning and a variety of other problems.
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Level Learning Set: A Novel Classifier Based on Active Contour Modelss in its ability to directly construct complex decision boundaries, and in better knowledge representation. Various experimental results including comparisons to existing machine learning algorithms are presented, and the advantages of the proposed approach are discussed.
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Learning Partially Observable Markov Models from First Passage Timesransitions with the lowest expected passage times are trimmed off from the model. Practical evaluations on artificially generated data and on DNA sequence modeling show the benefits over Bayesian model induction or EM estimation of ergodic models with transition trimming.
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Constraint Selection by Committee: An Ensemble Approach to Identifying Informative Constraints for Srmative, if known. An evaluation on text data shows that this provides an effective criterion for identifying constraints, leading to a reduction in the level of supervision required to direct a clustering algorithm to an accurate solution.
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