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  • During the last decade the super-modular Pair-wise Markov Networks (SM-PMN) have become a routinely used model for structured prediction. Their popularity can be attributed to efficient algorithms for the MAP inference. Comparably efficient algorithms for learning their parameters from data have not been available so far. We propose an instance of the Analytic Center Cutting Plane Method (ACCPM) for discriminative learning of the SM-PMN from annotated examples. We empirically evaluate the proposed ACCPM on a problem of learning the SM-PMN for image segmentation. Results obtained on two public datasets show that the proposed ACCPM significantly outperforms the current state-of-the-art algorithm in terms of computational time as well as the accuracy because it can learn models which were not tractable by existing methods.
  • During the last decade the super-modular Pair-wise Markov Networks (SM-PMN) have become a routinely used model for structured prediction. Their popularity can be attributed to efficient algorithms for the MAP inference. Comparably efficient algorithms for learning their parameters from data have not been available so far. We propose an instance of the Analytic Center Cutting Plane Method (ACCPM) for discriminative learning of the SM-PMN from annotated examples. We empirically evaluate the proposed ACCPM on a problem of learning the SM-PMN for image segmentation. Results obtained on two public datasets show that the proposed ACCPM significantly outperforms the current state-of-the-art algorithm in terms of computational time as well as the accuracy because it can learn models which were not tractable by existing methods. (en)
Title
  • Learning Markov Networks by Analytic Center Cutting Plane Method
  • Learning Markov Networks by Analytic Center Cutting Plane Method (en)
skos:prefLabel
  • Learning Markov Networks by Analytic Center Cutting Plane Method
  • Learning Markov Networks by Analytic Center Cutting Plane Method (en)
skos:notation
  • RIV/68407700:21230/12:00200373!RIV13-MSM-21230___
http://linked.open...avai/predkladatel
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(7E10047)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 146646
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/12:00200373
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Machine Learning and Data Mining; Statistical; Syntactic and Structural Pattern Recognition; Segmentation; Color and Texture (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [5C33FEBA3D96]
http://linked.open...v/mistoKonaniAkce
  • Tsukuba
http://linked.open...i/riv/mistoVydani
  • New York
http://linked.open...i/riv/nazevZdroje
  • ICPR 2012: Proceedings of 21st International Conference on Pattern Recognition
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Hlaváč, Václav
  • Franc, Vojtěch
  • Antoniuk, Kostiantyn
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • IEEE
https://schema.org/isbn
  • 978-4-9906441-0-9
http://localhost/t...ganizacniJednotka
  • 21230
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