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 methodsâall accessible through the software Râby 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.
Description
The support vector machine under test 10.1016/S0925-2312(03)00431-4 : Neurocomputing | ScienceDirect.com
%0 Journal Article
%1 Meyer2003169
%A Meyer, David
%A Leisch, Friedrich
%A Hornik, Kurt
%D 2003
%J Neurocomputing
%K bachelor:2011:bachmann classifier evaluierung svm test
%N 1â2
%P 169 - 186
%R 10.1016/S0925-2312(03)00431-4
%T The support vector machine under test
%U http://www.sciencedirect.com/science/article/pii/S0925231203004314
%V 55
%X 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 methodsâall accessible through the software Râby 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{Meyer2003169,
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 methodsâall accessible through the software Râby 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.},
added-at = {2012-01-17T14:50:53.000+0100},
author = {Meyer, David and Leisch, Friedrich and Hornik, Kurt},
biburl = {https://www.bibsonomy.org/bibtex/26103bd3731e94193fb3977731e689d12/telekoma},
description = {The support vector machine under test 10.1016/S0925-2312(03)00431-4 : Neurocomputing | ScienceDirect.com},
doi = {10.1016/S0925-2312(03)00431-4},
interhash = {1c6749d4ecb2b42ac7d2fe33d54c9cd5},
intrahash = {6103bd3731e94193fb3977731e689d12},
issn = {0925-2312},
journal = {Neurocomputing},
keywords = {bachelor:2011:bachmann classifier evaluierung svm test},
note = {<ce:title>Support Vector Machines</ce:title>},
number = {1â2},
pages = {169 - 186},
timestamp = {2012-01-17T14:50:53.000+0100},
title = {The support vector machine under test},
url = {http://www.sciencedirect.com/science/article/pii/S0925231203004314},
volume = 55,
year = 2003
}