{"quality_controlled":"1","publication_identifier":{"isbn":["978-331958770-7"]},"author":[{"first_name":"Vera","last_name":"Trajkovska","full_name":"Trajkovska, Vera"},{"first_name":"Paul","last_name":"Swoboda","id":"446560C6-F248-11E8-B48F-1D18A9856A87","full_name":"Swoboda, Paul"},{"first_name":"Freddie","last_name":"Åström","full_name":"Åström, Freddie"},{"first_name":"Stefanie","last_name":"Petra","full_name":"Petra, Stefanie"}],"month":"01","scopus_import":1,"editor":[{"full_name":"Lauze, François","last_name":"Lauze","first_name":"François"},{"full_name":"Dong, Yiqiu","last_name":"Dong","first_name":"Yiqiu"},{"last_name":"Bjorholm Dahl","first_name":"Anders","full_name":"Bjorholm Dahl, Anders"}],"language":[{"iso":"eng"}],"page":"323 - 334","date_updated":"2021-01-12T08:07:23Z","intvolume":"     10302","title":"Graphical model parameter learning by inverse linear programming","department":[{"_id":"VlKo"}],"year":"2017","day":"01","date_published":"2017-01-01T00:00:00Z","citation":{"chicago":"Trajkovska, Vera, Paul Swoboda, Freddie Åström, and Stefanie Petra. “Graphical Model Parameter Learning by Inverse Linear Programming.” edited by François Lauze, Yiqiu Dong, and Anders Bjorholm Dahl, 10302:323–34. Springer, 2017. <a href=\"https://doi.org/10.1007/978-3-319-58771-4_26\">https://doi.org/10.1007/978-3-319-58771-4_26</a>.","short":"V. Trajkovska, P. Swoboda, F. Åström, S. Petra, in:, F. Lauze, Y. Dong, A. Bjorholm Dahl (Eds.), Springer, 2017, pp. 323–334.","ieee":"V. Trajkovska, P. Swoboda, F. Åström, and S. Petra, “Graphical model parameter learning by inverse linear programming,” presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark, 2017, vol. 10302, pp. 323–334.","ista":"Trajkovska V, Swoboda P, Åström F, Petra S. 2017. Graphical model parameter learning by inverse linear programming. SSVM: Scale Space and Variational Methods in Computer Vision, LNCS, vol. 10302, 323–334.","ama":"Trajkovska V, Swoboda P, Åström F, Petra S. Graphical model parameter learning by inverse linear programming. In: Lauze F, Dong Y, Bjorholm Dahl A, eds. Vol 10302. Springer; 2017:323-334. doi:<a href=\"https://doi.org/10.1007/978-3-319-58771-4_26\">10.1007/978-3-319-58771-4_26</a>","apa":"Trajkovska, V., Swoboda, P., Åström, F., &#38; Petra, S. (2017). Graphical model parameter learning by inverse linear programming. In F. Lauze, Y. Dong, &#38; A. Bjorholm Dahl (Eds.) (Vol. 10302, pp. 323–334). Presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark: Springer. <a href=\"https://doi.org/10.1007/978-3-319-58771-4_26\">https://doi.org/10.1007/978-3-319-58771-4_26</a>","mla":"Trajkovska, Vera, et al. <i>Graphical Model Parameter Learning by Inverse Linear Programming</i>. Edited by François Lauze et al., vol. 10302, Springer, 2017, pp. 323–34, doi:<a href=\"https://doi.org/10.1007/978-3-319-58771-4_26\">10.1007/978-3-319-58771-4_26</a>."},"publication_status":"published","alternative_title":["LNCS"],"_id":"641","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","status":"public","abstract":[{"lang":"eng","text":"We introduce two novel methods for learning parameters of graphical models for image labelling. The following two tasks underline both methods: (i) perturb model parameters based on given features and ground truth labelings, so as to exactly reproduce these labelings as optima of the local polytope relaxation of the labelling problem; (ii) train a predictor for the perturbed model parameters so that improved model parameters can be applied to the labelling of novel data. Our first method implements task (i) by inverse linear programming and task (ii) using a regressor e.g. a Gaussian process. Our second approach simultaneously solves tasks (i) and (ii) in a joint manner, while being restricted to linearly parameterised predictors. Experiments demonstrate the merits of both approaches."}],"publisher":"Springer","publist_id":"7147","type":"conference","date_created":"2018-12-11T11:47:39Z","doi":"10.1007/978-3-319-58771-4_26","conference":{"name":"SSVM: Scale Space and Variational Methods in Computer Vision","location":"Kolding, Denmark","end_date":"2017-06-08","start_date":"2017-06-04"},"oa_version":"None","volume":10302}