<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jancsary, Jeremy</style></author><author><style face="normal" font="default" size="100%">Nowozin, Sebastian</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Non-parametric crfs for image labeling</style></title><secondary-title><style face="normal" font="default" size="100%">NIPS Workshop Modern Nonparametric Methods in Machine Learning</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.nowozin.net/sebastian/papers/jancsary2012nonparametriccrf.pdf</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">2</style></number><pages><style face="normal" font="default" size="100%">1–5</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We introduce a powerful non-parametric image labeling framework, Regression Tree Fields (RTFs), and discuss its application to image restoration. The conditional structure and the parameters of our model are estimated from training data so as to directly optimize for popular performance measures, resulting in excellent predictive performance at low computational cost.</style></abstract></record></records></xml>