Additive support vector machines for pattern classification.
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 SVM modeling 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.
Doumpos
Michael
M
Department of Production Engineering and Management, Financial Engineering Laboratory, Technical University of Crete, University Campus, 73100 Chania, Greece. mdoumpos@dpem.tuc.gr
Zopounidis
Constantin
C
Golfinopoulou
Vassiliki
V
D000465:Algorithms; D001185:Artificial Intelligence; D016247:Information Storage and Retrieval / Q000379:methods*; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*