@article{vsvm,
	title = {A tutorial on \ν-support vector machines: Research Articles},
	address = {Chichester, UK, UK},
	author = {Pai-Hsuen Chen and Chih-Jen Lin and Bernhard Sch\"{o}lkopf},
	journal = {Appl. Stoch. Model. Bus. Ind.},
	number = {2},
	pages = {111--136},
	publisher = {John Wiley and Sons Ltd.},
	url = {http://vis.lbl.gov/~romano/mlgroup/papers/nusvmtutorial.pdf},
	volume = {21},
	year = {2005},
	biburl = {http://www.bibsonomy.org/bibtex/24849dc190c907bcb507aece582e76353/jil},
	description = {A tutorial on ν-support vector machines},
	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].},
	issn = {1524-1904}, doi = {http://dx.doi.org/10.1002/asmb.v21:2},
	keywords = {kernel svm trick tutorial }
}