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13:00 - 14:00 15 November 2011

Decision Forests for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning

Location

Lecture Theatre G08 | Roberts Building (link Map)
Malet Place | London | WC1E 7JE | United Kingdom

Open to: Academic

Speaker information: Dr Antonio Criminisi, Microsoft Research Cambridge. Abstract: In this paper we present a general model of decision forests and discuss how it can be used for a large variety of supervised and unsupervised tasks in machine learning and computer vision. Numerous examples will help explain and demonstrate how small variants of the basic forest model yield powerful algorithms for efficient: classification, regression, density estimation, manifold learning, semi-supervised learning and active learning. Details of exemplar real-world applications including human tracking in Microsoft XBox Kinect and semantic recognition of medical images are also presented. Please visit http://research.microsoft.com/en-us/groups/vision/decisionforests.aspx to see the slides and technical report.


Contact

Ron Gaston
Please email | R.Gaston@ucl.ac.uk