@InProceedings{Krizek-ICPR-2006,
  IS = { zkontrolovano 29 Dec 2006 },
  UPDATE  = { 2006-09-12 },
  author =      {K{\v r}{\'\i }{\v z}ek, Pavel and
                 Josef Kittler and
                 Hlav{\' a}{\v c}, V{\' a}clav},
  title =       {Feature selection based on the training set manipulation},
  year =        {2006},
  pages =       {658--661},
  booktitle =   {ICPR 2006: Proceedings of the 18th International
                 Conference on Pattern Recognition},
  editor =      {Werner, Bob},
  publisher =   {IEEE Computer Society},
  address =     {10662 Los Vaqueros Circle, P.O.Box 3014, Los Alamitos, USA},
  isbn =        {0-7695-2521-0},
  volume =      {2},
  book_pages =  {1270},
  month =       {August},
  day =         {20--24},
  venue =       {Hong Kong},
  organization ={IEEE},
  annote = {A novel filter feature selection technique is introduced.
    The method exploits the information conveyed by the evolution of
    the training samples weights similarly to the Adaboost
    algorithm. Features are selected on the basis of their individual
    merit using a simple error function. The weights dynamics and its
    effect on the error function are utilised to identify and remove
    redundant and irrelevant features.  In experiments we show that
    the performance of commonly employed learning algorithms using
    features selected by the proposed method is the same or better
    than that obtained with features selected by the traditional
    state-of-theart techniques.},
  keywords =    {Feature selection, dimensionality reduction},
  prestige =    {international},
  authorship =  {70-20-10 },
  project =     {GACR 102/03/0440, INTAS 04-77-7347, IST-004176},
  psurl = { fulltext (PDF),
poster (PDF) },
}