'Linear boundary' presentation slideshows

Machine Learning Techniques for HEP Data Analysis with T MVA

Machine Learning Techniques for HEP Data Analysis with T MVA. Andreas H oecker ( * ) (CERN) Seminar, LAL Orsay , June 21, 2007 . ( * ) On behalf of the author team: A. Hoecker, P. Speckmayer, J. Stelzer, F. Tegenfeldt, H. Voss, K. Voss

662 views • 48 slides

T MVA ? ToolKit for Multivariate Analysis with ROOT ?

T MVA ? ToolKit for Multivariate Analysis with ROOT ?. Kai Voss ( * ) Teilchenseminar, Bonn, April 26, 2007. ( * ) on behalf of A. Hoecker,J. Stelzer, F. Tegenfeldt, H. Voss, and many other contributors. Motivation / Outline. ROOT: is the analysis framework used by most (HEP)-physicists

571 views • 38 slides

T MVA A Toolkit for MultiVariate Data Analysis with ROOT

525 views • 34 slides

Beyond Linear Separability

424 views • 31 slides

PAC Learning VC Dimension (Most slides by courtesy of Prof . Carlos Guestrin )

PAC Learning VC Dimension (Most slides by courtesy of Prof . Carlos Guestrin ). 10-701/15-781 Machine Learning Fall 2010 Recitation by Leman Akoglu 11/4/10. We only care about the maximum number of points that can be classified exactly!. Other examples of VC dimension.

419 views • 22 slides

8. Hypotheses 8.4 Two more things

230 views • 17 slides