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Additive support vector machines for pattern classification

Zopounidis Konstantinos, Doumpos, Michael, Golfinopoulou ,V

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URIhttp://purl.tuc.gr/dl/dias/B3F1A843-FFF7-4434-803F-243849F9F456-
Αναγνωριστικόhttps://doi.org/10.1109/TSMCB.2006.887427-
Γλώσσαen-
Μέγεθος11 pagesen
ΤίτλοςAdditive support vector machines for pattern classificationen
ΔημιουργόςZopounidis Konstantinosen
ΔημιουργόςΖοπουνιδης Κωνσταντινοςel
ΔημιουργόςDoumpos, Michaelen
ΔημιουργόςGolfinopoulou ,Ven
ΕκδότηςInstitute of Electrical and Electronics Engineersen
ΠερίληψηSupport 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
ΤύποςPeer-Reviewed Journal Publicationen
ΤύποςΔημοσίευση σε Περιοδικό με Κριτέςel
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2015-10-28-
Ημερομηνία Δημοσίευσης2007-
Βιβλιογραφική ΑναφοράM. 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

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