Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/B3F1A843-FFF7-4434-803F-243849F9F456 | - |
| Αναγνωριστικό | https://doi.org/10.1109/TSMCB.2006.887427 | - |
| Γλώσσα | en | - |
| Μέγεθος | 11 pages | en |
| Τίτλος | Additive support vector machines for pattern classification | en |
| Δημιουργός | Zopounidis Konstantinos | en |
| Δημιουργός | Ζοπουνιδης Κωνσταντινος | el |
| Δημιουργός | Doumpos, Michael | en |
| Δημιουργός | Golfinopoulou ,V | en |
| Εκδότης | Institute of Electrical and Electronics Engineers | en |
| Περίληψη | Support 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 techniques | en |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2015-10-28 | - |
| Ημερομηνία Δημοσίευσης | 2007 | - |
| Βιβλιογραφική Αναφορά | M. 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.2006.887427 | en |
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