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SUMMARY:Random Forests: One tool for all your problems. - Novi Quadrianto 
 (University of Cambridge)\; Neil Houlsby (University of Cambridge)
DTSTART:20130704T140000Z
DTEND:20130704T153000Z
UID:TALK45334@talks.cam.ac.uk
CONTACT:Colorado Reed
DESCRIPTION:We hope to introduce the wonderful world of random forests. Ra
 ndom forests are one of the most successful ensemble methods in machine le
 arning with state-of-the-art performance in many application domains. It w
 orks by averaging several predictions of de-correlated trees. In this talk
 \, we will present a unifying view of random forest that is capable of sol
 ving almost all your learning problems: classification\, regression\, dens
 ity estimation\, manifold learning\, and semi-supervised learning. We will
  also discuss recent work on the mathematical forces behind the success of
  random forest. Several future directions will be mentioned.\n\nReferences
 :\n1) A. Criminisi\, J. Shotton\, E. Konukoglu\, Decision forests: A unifi
 ed framework for classification\, regression\, density estimation\, manifo
 ld learning and semi-supervised learning\, Foundations and Trends in Compu
 ter Graphics and Vision\, 2012 (available online as an MSR techinal report
 )\n2) G. Biau\, L. Devroye\, and G. Lugosi\, Consistency of Random Forests
  and Other Averaging Classifiers\, JMLR\, 2008\n3) G. Biau\, Analysis of a
  random forests model\, JMLR\, 2012\n\nThe RCC will be tutorial in nature\
 , and will assume no prior knowledge\, however\, familiarisation with the 
 notation in Section 2 of [1] would be useful\, and for those more theoreti
 cally inclined we recommend reading [3] as we shall skip most of the mathe
 matical details.
LOCATION:Engineering Department\, CBL Room 438
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