@inbook{3011,
  title = {Combining a Filter Method with SVMs},
  booktitle = {Feature Extraction: Foundations and Applications, Studies in Fuzziness and Soft Computing, Vol. 207},
  abstract = {Our goal for the competition (feature selection competition NIPS 2003) was to evaluate the usefulness of simple
  machine learning techniques. We decided to use the correlation criteria as a feature selection method and Support Vector Machines for the classification part. Here we explain how we chose the regularization parameter C of the SVM, how we determined the kernel parameter and how we estimated the number of features used for each data set. All analyzes were carried out on the
  training sets of the competition data. We choose the data set Arcene as an example
  to explain the approach step by step.
  In our view the point of this competition was the construction of a well performing
  classifier rather than the systematic analysis of a specific approach. This is why our
  search for the best classifier was only guided by the described methods and that we
  deviated from the road map at several occasions.
  All calculations were done with the software Spider [2004].},
  pages = {439-446},
  series = {Studies in Fuzziness and Soft Computing ; 207},
  editors = {I Guyon and M Nikravesh and S Gunn and LA Zadeh},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  year = {2006},
  slug = {3011},
  author = {Lal, TN. and Chapelle, O. and Sch{\"o}lkopf, B.}
}