Online modeling based on support vector machine
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Robust classifiers for data reduced via random projections
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Expert Systems with Applications: An International Journal
New multi-class classification method based on the SVDD model
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Chinese speech recognition based on a hybrid SVM and HMM architecture
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Neurocomputing
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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