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Graphical Model Parameter Learning by Inverse Linear Programming

  • Vera TrajkovskaEmail author
  • Paul Swoboda
  • Freddie Åström
  • Stefania Petra
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10302)

Abstract

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.

Keywords

Learn Approach Subgradient Method Linear Support Vector Machine Ground Truth Labelings Pairwise Potential 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Vera Trajkovska
    • 1
    Email author
  • Paul Swoboda
    • 2
  • Freddie Åström
    • 1
  • Stefania Petra
    • 3
  1. 1.Image and Pattern Analysis GroupHeidelberg UniversityHeidelbergGermany
  2. 2.Institute of Science and TechnologyKlosterneuburgAustria
  3. 3.Mathematical Imaging GroupHeidelberg UniversityHeidelbergGermany

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