Slide 1Basic Steps 1.Compute the x and y image derivatives 2.Classify each derivative as being caused by either shading or a reflectance change 3.Set derivatives with the…

Slide 1Inductive Learning in Less Than One Sequential Data Scan Wei Fan, Haixun Wang, and Philip S. Yu IBM T.J.Watson Shaw-hwa Lo Columbia University Slide 2 Problems Many…

1.Randomised Decision Forests Tae-Kyun Kim http://www.iis.ee.ic.ac.uk/icvl/ 1 BMVA 2014 Computer Vision Summer School 2. Randomised Forests in the field [Moosmann et al.,…

Slide 1 The Rate of Convergence of AdaBoost Indraneel Mukherjee Cynthia Rudin Rob Schapire Slide 2 AdaBoost (Freund and Schapire 97) Slide 3 Slide 4 Basic properties of AdaBoost’s…

Slide 1 Slide 2 Boosting Shai Raffaeli Seminar in mathematical biology http://www1.cs.columbia.edu/~freund/ Slide 3 Toy Example Computer receives telephone call Measures…

Slide 1 University of Washington 1 Boosting and predictive modeling Yoav Freund Columbia University Slide 2 University of Washington 2 What is “data mining”? Lots of…

The Rate of Convergence of AdaBoost The Rate of Convergence of AdaBoost Indraneel Mukherjee Cynthia Rudin Rob Schapire This talk is about the rate of convergence of AdaBoost…

Robust real-time face detection Paul A. Viola and Michael J. Jones Intl. J. Computer Vision 57(2), 137–154, 2004 (originally in CVPR’2001) (slides adapted from Bill Freeman,…