@InProceedings{id:Nalepa2015Towards,
  Title                    = {Towards Parameter-less Support Vector Machines},
  Author                   = {Jakub Nalepa and Krzysztof Siminski and Michal Kawulok},
  Booktitle                = {2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)},
  Year                     = {2015},
  pages                    = {211-215},
  abstract  = {Support vector machines (SVMs) are a widely-used 
  machine learning technique, but they suffer from a significant
drawback of high time and memory training complexity,
which should be endured especially in big data problems.
SVMs incorporate kernel functions—it involves selecting
the kernel and induces an additional computational effort.
In this paper, we address these issues and propose an SVM
framework that automatically determines the kernel and 
selects data to train SVMs. It embodies the neuro-fuzzy system
for creating the kernel along with the memetic algorithm to
select training samples. Extensive experiments indicate that
our approach enables obtaining high classification scores.},
}