{"id":211,"date":"2015-07-17T11:49:50","date_gmt":"2015-07-17T10:49:50","guid":{"rendered":"http:\/\/babel.isa.uma.es\/kipr\/?p=211"},"modified":"2015-07-17T11:49:50","modified_gmt":"2015-07-17T10:49:50","slug":"brief-but-nice-related-work-about-structured-prediction-mrfs-crfs-etc","status":"publish","type":"post","link":"https:\/\/babel.isa.uma.es\/kipr\/?p=211","title":{"rendered":"Brief but nice related work about structured prediction (MRFs, CRFs, etc.)"},"content":{"rendered":"<h4>Bratieres, S.; Quadrianto, N.; Ghahramani, Z., <strong>GPstruct: Bayesian Structured Prediction Using Gaussian Processes,<\/strong> Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.37, no.7, pp.1514,1520, July 1 2015, <a href=\"doi.org\/10.1109\/TPAMI.2014.2366151\" target=\"_blank\">DOI: 10.1109\/TPAMI.2014.2366151<\/a>.<\/h4>\n<blockquote><p>We introduce a conceptually novel structured prediction model, GPstruct, which is kernelized, non-parametric and Bayesian, by design. We motivate the model with respect to existing approaches, among others, conditional random fields (CRFs), maximum margin Markov networks (M ^3 N), and structured support vector machines (SVMstruct), which embody only a subset of its properties. We present an inference procedure based on Markov Chain Monte Carlo. The framework can be instantiated for a wide range of structured objects such as linear chains, trees, grids, and other general graphs. As a proof of concept, the model is benchmarked on several natural language processing tasks and a video gesture segmentation task involving a linear chain structure. We show prediction accuracies for GPstruct which are comparable to or exceeding those of CRFs and SVMstruct.<\/p><\/blockquote>\n","protected":false},"excerpt":{"rendered":"<p>Bratieres, S.; Quadrianto, N.; Ghahramani, Z., GPstruct: Bayesian Structured Prediction Using Gaussian Processes, Pattern Analysis and Machine Intelligence, IEEE Transactions <span class=\"ellipsis\">&hellip;<\/span> <span class=\"more-link-wrap\"><a href=\"https:\/\/babel.isa.uma.es\/kipr\/?p=211\" class=\"more-link\"><span>Read More &rarr;<\/span><\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[64],"tags":[29,38,99,100,98],"class_list":["post-211","post","type-post","status-publish","format-standard","hentry","category-computer-vision","tag-bayesian-estimation","tag-bayesian-networks","tag-conditional-random-fields","tag-gaussian-processes","tag-markov-random-fields"],"_links":{"self":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/211"}],"collection":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=211"}],"version-history":[{"count":1,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/211\/revisions"}],"predecessor-version":[{"id":212,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/211\/revisions\/212"}],"wp:attachment":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=211"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=211"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=211"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}