The support vector machine under test

David Meyer

doi:10.1016/S0925-2312(03)00431-4

2003-09-01

666

PRE

David Meyer

Friedrich Leisch

Kurt Hornik

DOI：10.1016/S0925-2312(03)00431-4

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摘要

En 中文

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.

Keyword:

benchmark

comparative study

support vector machines

regression

classification

期刊

Neurocomputing

IF:

6.5

论文数:

2.5W

被引数:

6.5W

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暂无机构信息

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