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  • This report proposes a novel optimization algorithm for learning support vector machines (SVM) classifiers with structured output spaces introduced recently by Tsochantaridis et. al. Learning structural SVM classifier leads to a special instance of quadratic programming (QP) optimization with a huge number of constraints. The number of constraints is proportional to the cardinality of the output space which makes the QP task intractable by classical optimization methods. We propose a novel QP solver based on sequential minimal optimization (SMO). Unlike the original SMO, we propose a novel strategy for selecting variables to be optimized. The strategy aims at selecting such variables which yield the maximal improvement of optimization. We prove that the algorithm converges in a finite number of iterations to the solution which differs from the optimal one at most by a prescribed constant.
  • This report proposes a novel optimization algorithm for learning support vector machines (SVM) classifiers with structured output spaces introduced recently by Tsochantaridis et. al. Learning structural SVM classifier leads to a special instance of quadratic programming (QP) optimization with a huge number of constraints. The number of constraints is proportional to the cardinality of the output space which makes the QP task intractable by classical optimization methods. We propose a novel QP solver based on sequential minimal optimization (SMO). Unlike the original SMO, we propose a novel strategy for selecting variables to be optimized. The strategy aims at selecting such variables which yield the maximal improvement of optimization. We prove that the algorithm converges in a finite number of iterations to the solution which differs from the optimal one at most by a prescribed constant. (en)
  • This report proposes a novel optimization algorithm for learning support vector machines (SVM) classifiers with structured output spaces introduced recently by Tsochantaridis et. al. Learning structural SVM classifier leads to a special instance of quadratic programming (QP) optimization with a huge number of constraints. The number of constraints is proportional to the cardinality of the output space which makes the QP task intractable by classical optimization methods. We propose a novel QP solver based on sequential minimal optimization (SMO). Unlike the original SMO, we propose a novel strategy for selecting variables to be optimized. The strategy aims at selecting such variables which yield the maximal improvement of optimization. We prove that the algorithm converges in a finite number of iterations to the solution which differs from the optimal one at most by a prescribed constant. (cs)
Title
  • A Novel Algorithm for Learning Support Vector Machines with Structured Output Spaces
  • A Novel Algorithm for Learning Support Vector Machines with Structured Output Spaces (en)
  • A Novel Algorithm for Learning Support Vector Machines with Structured Output Spaces (cs)
skos:prefLabel
  • A Novel Algorithm for Learning Support Vector Machines with Structured Output Spaces
  • A Novel Algorithm for Learning Support Vector Machines with Structured Output Spaces (en)
  • A Novel Algorithm for Learning Support Vector Machines with Structured Output Spaces (cs)
skos:notation
  • RIV/68407700:21230/06:03124618!RIV09-MSM-21230___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(1M0567)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
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  • 463723
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  • RIV/68407700:21230/06:03124618
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http://linked.open.../riv/klicovaSlova
  • quadratic programming; structural classification; support vector machines (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [4B788F61A78D]
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
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http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Hlaváč, Václav
  • Franc, Vojtěch
http://localhost/t...ganizacniJednotka
  • 21230
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