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  • Není k dispozici (cs)
  • This contribution discusses one aspect of statistical learning and generalization. Theory of learning is very relevant to cognitive systems including cognitive vision. A technique allowing to approximate a huge training set 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 (PCA). In contrast to the PCA, the basis vectors of the low dimensional space used for data representation 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. As the computations in the proposed algorithm appear in a form of dot product, kernel methods can be used to cope with non-linear problems. The proposed method was tested to approximate training sets of the Support Vector Machines and Kernel Fisher Linear Discr
  • This contribution discusses one aspect of statistical learning and generalization. Theory of learning is very relevant to cognitive systems including cognitive vision. A technique allowing to approximate a huge training set 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 (PCA). In contrast to the PCA, the basis vectors of the low dimensional space used for data representation 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. As the computations in the proposed algorithm appear in a form of dot product, kernel methods can be used to cope with non-linear problems. The proposed method was tested to approximate training sets of the Support Vector Machines and Kernel Fisher Linear Discr (en)
Title
  • Greedy Kernel Principal Component Analysis
  • Není k dispozici (cs)
  • Greedy Kernel Principal Component Analysis (en)
skos:prefLabel
  • Greedy Kernel Principal Component Analysis
  • Není k dispozici (cs)
  • Greedy Kernel Principal Component Analysis (en)
skos:notation
  • RIV/68407700:21230/06:03124623!RIV07-GA0-21230___
http://linked.open.../vavai/riv/strany
  • 87 ; 106
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA102/03/0440)
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
  • 477299
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/06:03124623
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Kernel Methods; Principal Component Analysis (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [3BB58524B379]
http://linked.open...v/mistoKonaniAkce
  • Dagstuhl Castle
http://linked.open...i/riv/mistoVydani
  • Heidelberg
http://linked.open...i/riv/nazevZdroje
  • Cognitive Vision Systems
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
  • Franc, Vojtěch
  • Hlaváe, Václav
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • Springer-Verlag
https://schema.org/isbn
  • 3-540-33971-X
http://localhost/t...ganizacniJednotka
  • 21230
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