%0 Journal Article %1 vsvm %A Chen, Pai-Hsuen %A Lin, Chih-Jen %A Schölkopf, Bernhard %C Chichester, UK, UK %D 2005 %I John Wiley and Sons Ltd. %J Appl. Stoch. Model. Bus. Ind. %K kernel svm trick tutorial %N 2 %P 111--136 %R http://dx.doi.org/10.1002/asmb.v21:2 %T A tutorial on \ν-support vector machines: Research Articles %U http://vis.lbl.gov/~romano/mlgroup/papers/nusvmtutorial.pdf %V 21 %X We briefly describe the main ideas of statistical learning theory, support vector machines (SVMs), and kernel feature spaces. We place particular emphasis on a description of the so-called ν-SVM, including details of the algorithm and its implementation, theoretical results, and practical applications. Copyright © 2005 John Wiley & Sons, Ltd.Parts of the present article are based on 1.

@article{vsvm, abstract = {We briefly describe the main ideas of statistical learning theory, support vector machines (SVMs), and kernel feature spaces. We place particular emphasis on a description of the so-called ν-SVM, including details of the algorithm and its implementation, theoretical results, and practical applications. Copyright © 2005 John Wiley & Sons, Ltd.Parts of the present article are based on [1].}, added-at = {2008-05-02T00:49:56.000+0200}, address = {Chichester, UK, UK}, author = {Chen, Pai-Hsuen and Lin, Chih-Jen and Sch\"{o}lkopf, Bernhard}, biburl = {https://www.bibsonomy.org/bibtex/24849dc190c907bcb507aece582e76353/jil}, description = {nice view of the polynomial kernel trick }, doi = {http://dx.doi.org/10.1002/asmb.v21:2}, interhash = {1d90db8d7ae25986c2ce896b4fce3ba3}, intrahash = {4849dc190c907bcb507aece582e76353}, issn = {1524-1904}, journal = {Appl. Stoch. Model. Bus. Ind.}, keywords = {kernel svm trick tutorial}, number = 2, pages = {111--136}, publisher = {John Wiley and Sons Ltd.}, timestamp = {2013-11-23T20:11:51.000+0100}, title = {A tutorial on \ν-support vector machines: Research Articles}, url = {http://vis.lbl.gov/~romano/mlgroup/papers/nusvmtutorial.pdf}, volume = 21, year = 2005 }