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The support vector machine under test
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DOI：10.1016/S0925-2312(03)00431-4.png)
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Abstract
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Support vector machines (SVMs) are rarely benchmarked against other classification or regression methods. We compare a popular SVM implementation (libsvm) to 16 classification methods and 9 regression methods-all accessible through the software R-by the means of standard performance measures (classification error and mean squared error) which are also analyzed by the means of bias-variance decompositions. SVMs showed mostly good performances both on classification and regression tasks, but other methods proved to be very competitive. (C) 2003 Elsevier B.V. All rights reserved.
Keywords:
benchmark
comparative study
support vector machines
regression
classification
Journal
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6.5
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2.5W
Citations:
6.5W
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