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Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/B3F1A843-FFF7-4434-803F-243849F9F456 | ||
| Year | 2007 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
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| Bibliographic Citation | 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 https://doi.org/10.1109/TSMCB.2006.887427 | ||
| Appears in Collections |
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
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