{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bayesian-structured-prediction-using-gaussian","title":"Bayesian Structured Prediction Using Gaussian Processes","arxiv_id":"1307.3846","date":"2013-07-15","proceeding":null,"authors":["Sebastien Bratieres","Novi Quadrianto","Zoubin Ghahramani"],"abstract":"We introduce a conceptually novel structured prediction model, GPstruct,\nwhich is kernelized, non-parametric and Bayesian, by design. We motivate the\nmodel with respect to existing approaches, among others, conditional random\nfields (CRFs), maximum margin Markov networks (M3N), and structured support\nvector machines (SVMstruct), which embody only a subset of its properties. We\npresent an inference procedure based on Markov Chain Monte Carlo. The framework\ncan be instantiated for a wide range of structured objects such as linear\nchains, trees, grids, and other general graphs. As a proof of concept, the\nmodel is benchmarked on several natural language processing tasks and a video\ngesture segmentation task involving a linear chain structure. We show\nprediction accuracies for GPstruct which are comparable to or exceeding those\nof CRFs and SVMstruct.","url_abs":"http://arxiv.org/abs/1307.3846v1","url_pdf":"http://arxiv.org/pdf/1307.3846v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"bayesian-structured-prediction-using-gaussian","repo_url":"https://github.com/sebastien-bratieres/pygpstruct","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"structured-prediction","task_name":"Structured Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}