Titlebook: Applications of Supervised and Unsupervised Ensemble Methods; Oleg Okun,Giorgio Valentini Book 2009 Springer-Verlag Berlin Heidelberg 2009

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An Ensemble Pruning Primer,resentative methods of each category. It abstracts their key components and discusses their main advantages and disadvantages. We hope that this work will serve as a good starting point and reference for researchers working on the development of new ensemble pruning methods.

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Clustering Ensembles with Active Constraints, during the partitioning process. The experimental results show that our constrained ensemble technique is capable of producing a partition that is as good as, or better, than those computed by other semi-supervised clustering approaches.

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Integrating Liknon Feature Selection and Committee Training,In problems, characterized by nonlinear class separation, the Liknon-selected feature profiles, optimal for linear class separation, also identify the relevant feature subspaces. The capabilities of the proposed approach are illustrated in a benchmark of NIPS2003 feature selection challenge.