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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 target values.Cited by: 313
https://www.csie.ntu.edu.tw/~cjlin/papers/newsvr.pdf
Training ν-Support Vector Regression: Theory and Algorithms Chih-Chung Chang and Chih-Jen Lin Department of Computer Science and Information Engineering National Taiwan University Taipei 106, Taiwan ([email protected]) Abstract We discuss the relation between ǫ-Support Vector Regression (ǫ-SVR) and ν-Support VectorRegression (ν-SVR).Cited by: 313
http://ntur.lib.ntu.edu.tw/bitstream/246246/155217/1/09.pdf
Training ν-Support Vector Classifiers: Theory and Algorithms Chih-Chung Chang Chih-Jen Lin Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan The ν-support vector machine (ν-SVM) for classification proposed by Sch¨olkopf, Smola, Williamson, and Bartlett (2000) has the advantage of
https://www.semanticscholar.org/paper/Training-nu-support-vector-regression%3A-theory-and-Chang-Lin/01ef33ec55ad9614a7f2c2c8e5881e327190c763
We discuss the relation between epsilon-support vector regression (epsilon-SVR) and nu-support vector regression (nu-SVR). In particular, we focus on properties that are different from those of C-support vector classification (C-SVC) and nu-support vector classification (nu-SVC). We then discuss some issues that do not occur in the case of classification: the possible range of epsilon and the ...
https://dl.acm.org/citation.cfm?id=639737
Home Browse by Title Periodicals Neural Computation Vol. 14, No. 8 Training v-support vector regression: theory and algorithms article Training v -support vector regression: theory and algorithmsCited by: 5
https://www.mathworks.com/help/stats/understanding-support-vector-machine-regression.html
Understanding Support Vector Machine Regression Mathematical Formulation of SVM Regression Overview. Support vector machine (SVM) analysis is a popular machine learning tool for classification and regression, first identified by Vladimir Vapnik and his colleagues in 1992.SVM regression is considered a nonparametric technique because it relies on kernel functions.
https://www.csie.ntu.edu.tw/~cjlin/papers.html
A comparison of methods for multi-class support vector machines , IEEE Transactions on Neural Networks, 13(2002), 415-425. C.-C. Chang and C.-J. Lin. Training nu-support vector regression: theory and algorithms , Neural Computation, 14(2002), 1959-1977. Implementation available in libsvm .
http://ce.sharif.ir/courses/85-86/2/ce725/resources/root/LECTURES/SVM.pdf
ically used to describe classification with support vector methods and support vector regression is used to describe regression with support vector methods. In this report the term SVM will refer to both classification and regression methods, and the terms Support Vector Classification (SVC) and Support Vector Regression (SVR) will be used
https://www.academia.edu/2834342/Training_suport_vector_regression_Theory_and_algorithms
We discuss the relation betweenϵ-support vector regression (ϵ-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) and v-support vector
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