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Description
  • A technique for a training set approximation and its usage in kernel methods is proposed. The approach aims to represent data in a low dimensional space with possibly minimal representation error which is similar to the Principal Component Analysis. In contrast to the PCA, the basis vectors of the low dimensional space used for data approximation are properly selected vectors from the training set and not as their linear combinations. The basis vectors can be selected by a simple algorithm which has low computational requirements and allows on-line processing of huge data sets. The proposed method was used to approximate training sets of the Support Vector Machines and Kernel Fisher Linear Discriminant which are known method for learning classifiers. The experiments show that the proposed approximation can significantly reduce the complexity of the found classifiers while retaining their accuracy.
  • A technique for a training set approximation and its usage in kernel methods is proposed. The approach aims to represent data in a low dimensional space with possibly minimal representation error which is similar to the Principal Component Analysis. In contrast to the PCA, the basis vectors of the low dimensional space used for data approximation are properly selected vectors from the training set and not as their linear combinations. The basis vectors can be selected by a simple algorithm which has low computational requirements and allows on-line processing of huge data sets. The proposed method was used to approximate training sets of the Support Vector Machines and Kernel Fisher Linear Discriminant which are known method for learning classifiers. The experiments show that the proposed approximation can significantly reduce the complexity of the found classifiers while retaining their accuracy. (en)
Title
  • Training Set Approximation for Kernel Methods
  • Training Set Approximation for Kernel Methods (en)
skos:prefLabel
  • Training Set Approximation for Kernel Methods
  • Training Set Approximation for Kernel Methods (en)
skos:notation
  • RIV/68407700:21230/03:03087039!RIV/2004/GA0/212304/N
http://linked.open.../vavai/riv/strany
  • 121 ; 126
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA102/03/0440), Z(MSM 212300013)
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
  • 631267
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/03:03087039
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Kernel Methods;PCA;Pattern Recognition;SVM (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [E0B126B5246C]
http://linked.open...v/mistoKonaniAkce
  • Valtice
http://linked.open...i/riv/mistoVydani
  • Prague
http://linked.open...i/riv/nazevZdroje
  • Computer Vision - CVWW'03 : Proceedings of the 8th Computer Vision Winter Workshop
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
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • Czech Pattern Recognition Society
https://schema.org/isbn
  • 80-238-9967-8
http://localhost/t...ganizacniJednotka
  • 21230
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