IS = { zkontrolovano 18 Jan 2009 },
  UPDATE  = { 2008-12-16 },
  author =      {K{\' a}lal, Zdenek and
                 Matas, Ji{\v r}{\' \i} and
                 Mikolajczyk, Krystian},
  title =       {Weighted Sampling for Large-Scale Boosting},
  authorship =  {40-40-20},
  year =        {2008},
  pages =       {413--422},
  booktitle =   {BMVC 2008: Proceedings of the 19th British
                 Machine Vision Conference},
  volume =      {1},
  editor =      {Everingham, M. and Needham, C.  and Fraille, R.},
  isbn =        {978-1-901725-36-0},
  book_pages =  {1194},
  publisher =   {BMVA},
  address =     {London, UK},
  month =       {September},
  day =         {1--4},
  venue =       {Leeds, UK},
  project =     {GACR 201/06/1821},
  psurl =       {pdf},
  annote = {This paper addresses the problem of learning from very
    large databases where batch learning is impractical or even
    infeasible.  Bootstrap is a popular technique applicable in such
    situations. We show that sampling strategy used for bootstrapping
    has a significant impact on the resulting classifier
    performance. We design a new general sampling strategy
    quasi-random weighted sampling + trimming (QWS+) that includes
    well established strategies as special cases. The QWS+ approach
    minimizes the variance of hypothesis error estimate and leads to
    significant improvement in performance compared to standard
    sampling techniques. The superior performance is demonstrated on
    several problems including profile and frontal face detection.},
  keywords =    {machine learning, AdaBoost, importance sampling},