@InProceedings{franc-CVWW03,
  IS = { zkontrolovano 23 Apr 2003 },
  UPDATE  = { 2003-02-19 },
  author =       { Franc, Vojt{\v e }ch and Hlav{\'a}{\v c}, V{\'a}clav },
  title =        { Training Set Approximation for Kernel Methods },
  pages =        { 121-126 },
  booktitle =    { Computer Vision\,---\,CVWW'03~: Proceedings of the
                   8th Computer Vision Winter Workshop },
  isbn =         { 80-238-9967-8 },
  book_pages =   { 176 },
  year =         { 2003 },
  editor =       { Drbohlav, Ond{\v r}ej },
  publisher =    { Czech Pattern Recognition Society },
  address =      { Prague, Czech Republic },
  month =        { February },
  day =          { 3-6 },
  venue =        { Valtice, Czech Republic },
  authorship =   { 50-50 },
  project =      { MIRACLE ICA1-CT-2000-70002, IST-2001-32184 ActIPret, MSM 212300013,
                   GACR 102/03/0440},
  annote =       { 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. },
  keywords =     { Pattern Recognition, SVM, PCA, Kernel Methods },
  psurl =        { franc-CVWW03.pdf},
}