@inproceedings{f402ac4356f044beae2c3e8628b8dd65,
title = "Linear penalization Support Vector Machines for feature selection",
abstract = "Support Vector Machines have proved to be powerful tools for classification tasks combining the minimization of classification errors and maximizing their generalization capabilities. Feature selection, however, is not considered explicitly in the basic model formulation. We propose a linearly penalized Support Vector Machines (LP-SVM) model where feature selection is performed simultaneously with model construction. Its application to a problem of customer retention and a comparison with other feature selection techniques demonstrates its effectiveness.",
author = "Jaime Miranda and Ricardo Montoya and Richard Weber",
year = "2005",
doi = "10.1007/11590316\_24",
language = "Ingl{\'e}s",
isbn = "3540305068",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "188--192",
booktitle = "Pattern Recognition and Machine Intelligence - First International Conference, PReMI 2005, Proceedings",
note = "1st International Conference on Pattern Recognition and Machine Intelligence, PReMI 2005 ; Conference date: 20-12-2005 Through 22-12-2005",
}