Institutional Repository [SANDBOX]
Technical University of Crete
EN  |  EL

Search

Browse

My Space

Additive support vector machines for pattern classification

Zopounidis Konstantinos, Doumpos, Michael, Golfinopoulou ,V

Simple record

URIhttp://purl.tuc.gr/dl/dias/B3F1A843-FFF7-4434-803F-243849F9F456-
Identifierhttps://doi.org/10.1109/TSMCB.2006.887427-
Languageen-
Extent11 pagesen
TitleAdditive support vector machines for pattern classificationen
CreatorZopounidis Konstantinosen
CreatorΖοπουνιδης Κωνσταντινοςel
CreatorDoumpos, Michaelen
CreatorGolfinopoulou ,Ven
PublisherInstitute of Electrical and Electronics Engineersen
Content SummarySupport vector machines (SVMs) are one of the most popular methodologies for the design of pattern classification systems with sound theoretical foundations and high generalizing performance. The SVM framework focuses on linear and nonlinear models that maximize the separating margin between objects belonging in different classes. This paper extends the SVMmodeling context toward the development of additive models that combine the simplicity and transparency/interpretability of linear classifiers with the generalizing performance of nonlinear models. Experimental results are also presented on the performance of the new methodology over existing SVM techniquesen
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-10-28-
Date of Publication2007-
Bibliographic CitationM. Doumpos, C. Zopounidis, V. Golfinopoulou ," Additive support vector machines for pattern classification," IEEE Trans. of Systems, Man and Cyb. – Part B, vol. 37, no. 3,pp. 540 - 550,Ma. 2007.doi:10.1109/TSMCB.2006.887427en

Services

Statistics

 

Copyright © DIAS 2013