The support vector machine under test

by: David Meyer, Friedrich Leisch, Kurt Hornik

Neurocomputing, Vol. 55, No. 1-2. (September 2003), pp. 169-186.

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Abstract

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 --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.

@article{citeulike:2776513, abstract = {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 --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.}, author = {Meyer, David and Leisch, Friedrich and Hornik, Kurt }, booktitle = {Support Vector Machines}, citeulike-article-id = {2776513}, doi = {10.1016/S0925-2312(03)00431-4}, journal = {Neurocomputing}, keywords = {algebra, algorithms, kdd, optimization, stochastic}, month = {September}, number = {1-2}, pages = {169--186}, posted-at = {2008-05-09 21:39:06}, priority = {2}, title = {The support vector machine under test}, url = {http://dx.doi.org/10.1016/S0925-2312(03)00431-4}, volume = {55}, year = {2003} }

RIS record

TY - JOUR ID - citeulike:2776513 TI - The support vector machine under test T2 - Support Vector Machines JF - Neurocomputing VL - 55 IS - 1-2 SP - 169 EP - 186 N2 - 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 --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. KW - algebra KW - algorithms KW - kdd KW - optimization KW - stochastic AU - Meyer, David AU - Leisch, Friedrich AU - Hornik, Kurt PY - 2003/09// UR - http://dx.doi.org/10.1016/S0925-2312(03)00431-4 ER -

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