A support vector machine using the lazy learning approach for multi-class classification.

E Comak; A Arslan

doi:10.1080/03091900500095729

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A support vector machine using the lazy learning approach for multi-class classification.

E Comak·A Arslan

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Abstract

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.

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Algorithms Artificial Intelligence Computer Simulation Fuzzy Logic Models, Biological Models, Statistical Pattern Recognition, Automated Journal Article

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E Comak, A Arslan. A support vector machine using the lazy learning approach for multi-class classification.. https://doi.org/10.1080/03091900500095729

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