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http://ntur.lib.ntu.edu.tw/bitstream/246246/155217/1/09.pdf
Training ν-Support Vector Classifiers 2121 2 The Relation Between ν-SVM and C-SVM In this section we construct a relationship between Dν and DC; the main result is in theorem 5. The relation between DC and Dν has been discussed by Sch¨olkopf et al. (2000, Proposition 13), who show that if Pν leads to ρ>0, then PC with C = 1/(ρl) leads to the same decision function.
https://www.mitpressjournals.org/doi/10.1162/089976602760128081
We discuss the relation betweenɛ-support vector regression (ɛ-SVR) and v-support vector regression (v-SVR).In particular, we focus on properties that are different from those of C-support vector classification (C-SVC) andv-support vector classification (v-SVC).We then discuss some issues that do not occur in the case of classification: the possible range of ɛ and the scaling of …Cited by: 312
https://en.wikipedia.org/wiki/Support_vector_machine
The soft-margin support vector machine described above is an example of an empirical risk minimization (ERM) algorithm for the hinge loss. Seen this way, support vector machines belong to a natural class of algorithms for statistical inference, and many of its unique features are due to the behavior of the hinge loss.
https://link.springer.com/chapter/10.1007/0-387-24255-4_7
Burges, C.J.C. (1998), A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery, Vol. 2, no. 2. Google ScholarCited by: 23
https://www.csie.ntu.edu.tw/~cjlin/papers/newsvr.pdf
The ν-support vector machine (Sch¨olkopf et al. 2000; Sch¨olkopf et al. 1999) is a new class of support vector machines (SVM). It can handle both classification and regression. Properties on training ν-support vector classifiers (ν-SVC) have been discussed in (Chang and Lin 2001b). In this paper we focus on ν-support vector regression ...Cited by: 312
https://stats.stackexchange.com/questions/94118/difference-between-ep-svr-and-nu-svr-and-least-squares-svr
The difference between $\epsilon$-SVR and $\nu$-SVR is how the training problem is parametrized. Both use a type of hinge loss in the cost function. The $\nu$ parameter in $\nu$-SVM can be used to control the amount of support vectors in the resulting model. Given appropriate parameters, the exact same problem is solved. 1
https://www.csie.ntu.edu.tw/~cjlin/papers/nusvmtutorial.pdf
A Tutorial on ν-Support Vector Machines Pai-Hsuen Chen1, Chih-Jen Lin1, and Bernhard Scholkopf¨ 2? 1 Department of Computer Science and Information Engineering National Taiwan University Taipei 106, Taiwan 2 Max Planck Institute for Biological Cybernetics, Tubingen, Germany¨ [email protected] Abstract. We briefly describe the main …Cited by: 339
https://www.quora.com/What-is-the-difference-between-C-SVM-and-nu-SVM
Dec 19, 2016 · SVM use hyperplanes to perform classification. While performing classifications using SVM there are 2 types of SVM * C SVM * Nu SVM C and nu are regularisation parameters which help implement a penalty on the misclassifications that are performed ...
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