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The main ensemble methods discussed are Bayes model averaging, bagging, and stacking.
Clustering Stability: Impossibility and Possibility This presentation was addressed to a specialized audience of people in Data Mining and Machine Learning. The talk provided a theory that showed how clustering stability can be used to choose the correct number of clusters, as well as demonstrate the importance of cluster stability and discussed the use of […]
Clustering Impossibility and Stability This presentation was aimed at a general statistical audience and showed the importance of cluster stability (a theorem that in the limit of large dimensions can get complete noninformativity of clustering for finite sample sizes) and discussed the use of stability for clustering evaluation. Bertrand Salem Clarke (UM), Hoyt Koepke (UM […]
Review of Bayesian and Frequentist Statistics This presentation was part 1 of a 4-part course given on the basics of machine learning. This first course was a review of standard graduate statistics material. Bertrand Salem Clarke (UM) Bertrand Salem Clarke (UM)Notre Dame University Conference Louaize, Lebanon | July 4-9, 2011 NDU 2011-Pt.1 PowerPoint Slides […]