dc.contributor.author | Assaleh, Khaled | |
dc.contributor.author | Shanableh, Tamer | |
dc.date.accessioned | 2017-05-04T11:10:26Z | |
dc.date.available | 2017-05-04T11:10:26Z | |
dc.date.issued | 2010 | |
dc.identifier.citation | Assaleh, K. & Shanableh, T. (2010). Robust polynomial classifier using L1-norm minimization. Applied Intelligence, 33(3), 330-339. doi:10.1007/s10489-009-0169-8 | en_US |
dc.identifier.issn | 1573-7497 | |
dc.identifier.uri | http://hdl.handle.net/11073/8833 | |
dc.description.abstract | In this paper we present a robust polynomial classifier based on L1-norm minimization. We do so by reformulating the classifier training process as a linear programming problem. Due to the inherent insensitivity of the L1-norm to influential observations, class models obtained via L1-norm minimization are much more robust than their counterparts obtained by the classical least squares minimization (L2-norm). For validation purposes, we apply this method to two recognition problems: character recognition and sign language recognition. Both are examined under different signal to noise ratio (SNR) values of the test data. Results show that L1-norm minimization provides superior recognition rates over L2-norm minimization when the training data contains influential observations especially if the test dataset is noisy. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | Springer | en_US |
dc.relation.uri | http://dx.doi.org/10.1007/s10489-009-0169-8 | en_US |
dc.subject | Polynomial classifier | en_US |
dc.subject | Multivariate regression | en_US |
dc.subject | Pattern classification | en_US |
dc.title | Robust Polynomial Classifier Using L1-norm minimization | en_US |
dc.type | Article | en_US |
dc.type | Postprint | en_US |
dc.type | Peer-Reviewed | en_US |
dc.identifier.doi | 10.1007/s10489-009-0169-8 | |