A support vector machine using the lazy learning approach for multi-class classification.
Support vector machines can be used in a new machine learning technique based on statistical learning. In this paper, we develop least squares support vector machines (LS-SVMs) using the lazy learning approach to classify data in unclassifiable regions in the case of multi-class classification. LS-SVMs use a set of linear equations while SVMs use a quadratic programming problem. The lazy learning approach is a local and memory-based technique. Therefore, it is an alternative technique to fuzzy inference systems. Our studies show that LS-SVMs with the lazy learning approach can give comparable results to fuzzy LS-SVMs for multi-class classification.
Department of Computer Engineering, Engineering and Architecture Faculty, Selcuk University, Konya, Turkey. ecomak@selcuk.edu.tr
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D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D017143:Fuzzy Logic; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*