@Article{IK13,
  AUTHOR =   {Iveta Mr{\'a}zov{\'a} and Marek Kuka{\v C}ka},
  TITLE =    {Image Classification with Growing Neural Networks},
  JOURNAL =  {IJCTE - International Journal of Computer Theory and Engineering},
  VOLUME =   {5},
  PAGES =    {422--427},
  YEAR =     {2013},
  URL =      {http://dx.doi.org/10.7763/IJCTE.2013.V5.722},
  ABSTRACT = {Future multi-media technologies are expected to support efficient 
              on-line processing of huge amounts of high-dimensional data without 
              any special pre-processing. In this paper, we will introduce a new 
              model of the so-called Growing Hierarchical Neural Networks (GHNN) 
              applicable to image classification without requiring advanced 
              domain-specific feature extraction techniques. It can be, moreover, 
              supposed that the involved dynamic data-dependent adjustment of both 
              the number and position of the neurons improves generalization. 
              Experimental results obtained so far for two case studies on face and 
              hand-written digit recognition show that local features detected 
              automatically by GHNN-networks impact a transparent and compact 
              representation of the extracted knowledge.},
}