Data Mining Techniques to classify inter-area oscillations Adamantios Marinakis ABB Corporate Research CH London, 29/11/2013.
Based on slides by Pierre Dönnes and Ron Meir Modified by Longin Jan Latecki, Temple University Ch. 5: Support Vector Machines Stephen Marsland, Machine.
CSI :Florida A BAYESIAN APPROACH TO LOCALIZED MULTI-KERNEL LEARNING USING THE RELEVANCE VECTOR MACHINE R. Close, J. Wilson, P. Gader.
Condition Monitoring of Variable State Machinery
Super Vector Machine(SVM) with Iris and Mushroom Dataset
ABayesianApproachToLocalizedMultiKernelLearningUsingTheRelevanceVectorMachine.pptx
Identification of Relevant Sections in Web Pages Using a Machine Learning Approach
Input Space versus Feature Space in Kernel- Based Methods Scholkopf, Mika, Burges, Knirsch, Muller, Ratsch, Smola presented by: Joe Drish Department of.
Basics of Kernel Methods in Statistical Learning Theory Mohammed Nasser Professor Department of Statistics Rajshahi University E-mail: [email protected].
Viola 2003 Learning and Vision: Discriminative Models Chris Bishop and Paul Viola.
Crash Course on Machine Learning Part IV Several slides from Derek Hoiem, and Ben Taskar.
Perspectives on System Identification Lennart Ljung Linköping University, Sweden.
