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  • In a variety of applications, kernel machines such as Support Vector Machines (SVMs) have been used with great success often delivering state-of-the-art results. Using the kernel trick, they work on several domains and even enable heterogeneous data fusion by concatenating feature spaces or multiple kernel learning. Unfortunately, they are not suited for truly large-scale applications since they suffer from the curse of supporting vectors, e.g., the speed of applying SVMs decays linearly with the number of support vectors. In this paper we develop COFFIN - a new training strategy for linear SVMs that effectively allows the use of on demand computed kernel feature spaces and virtual examples in the primal. With linear training and prediction effort this framework leverages SVM applications to truly large-scale problems: As an example, we train SVMs for human splice site recognition involving 50 million examples and sophisticated string kernels.
  • In a variety of applications, kernel machines such as Support Vector Machines (SVMs) have been used with great success often delivering state-of-the-art results. Using the kernel trick, they work on several domains and even enable heterogeneous data fusion by concatenating feature spaces or multiple kernel learning. Unfortunately, they are not suited for truly large-scale applications since they suffer from the curse of supporting vectors, e.g., the speed of applying SVMs decays linearly with the number of support vectors. In this paper we develop COFFIN - a new training strategy for linear SVMs that effectively allows the use of on demand computed kernel feature spaces and virtual examples in the primal. With linear training and prediction effort this framework leverages SVM applications to truly large-scale problems: As an example, we train SVMs for human splice site recognition involving 50 million examples and sophisticated string kernels. (en)
Title
  • COFFIN: A Computational Framework for Linear SVMs
  • COFFIN: A Computational Framework for Linear SVMs (en)
skos:prefLabel
  • COFFIN: A Computational Framework for Linear SVMs
  • COFFIN: A Computational Framework for Linear SVMs (en)
skos:notation
  • RIV/68407700:21230/09:00166480!RIV10-MSM-21230___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • R
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
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  • 307451
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/09:00166480
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • support vector machines; large-scale learning; classification (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [7B60A5B70CE1]
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Franc, Vojtěch
http://localhost/t...ganizacniJednotka
  • 21230
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