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https://datascience.stackexchange.com/questions/5322/cost-function-for-support-vector-regression
Cost function for support vector regression. Ask Question Asked 4 years, 9 months ago. Active 4 years, 8 months ... the flatter the function, the simpler or more parsimonious it is. This applies to both classification and regression. share improve this answer. answered Mar 15 '15 at 18:53. Emre Emre. 9,086 1 1 gold badge 23 23 silver badges ...
http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.2760
Abstract. The concept of Support Vector Regression is extended to a more general class of convex cost functions. Moreover it is shown how the resulting convex constrained optimization problems can be efficiently solved by a Primal-Dual Interior Point path following method.
http://www.kernel-machines.org/publications/SmoSchMul98/bibliography_exportForm
The concept of Support Vector Regression is extended to a more general class of convex cost functions. Moreover it is shown how the resulting convex constrained optimization problems can be efficiently solved by a Primal–Dual Interior Point path following method. Both computational feasibility and improvement of estimation is demonstrated in the experiments.
https://link.springer.com/article/10.1007/s40565-018-0397-1
Mar 07, 2018 · General noise cost functions have been recently proposed for support vector regression (SVR). When applied to tasks whose underlying noise distribution is similar to the one assumed for the cost function, these models should perform better than classical $$\\epsilon$$ ϵ -SVR. On the other hand, uncertainty estimates for SVR have received a somewhat limited attention in the …Cited by: 3
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.673.8924
General Cost Functions for Support Vector Regression Alex J Smola ... @MISC{First_generalcost, author = {Gmd First and Rudower Chaussee}, title = {General Cost Functions for Support Vector Regression Alex J Smola}, year = {}} Share. OpenURL . Abstract. fsmola bs klausg. Keyphrases. general cost function support vector regression alex smola ...
https://alex.smola.org/papers/1998/SmoSchMul98.pdf
ector Regression is extended to a more general class of con v ex cost functions It is sho wn ho w the resulting con ex con strained optimization problems can b e ecien ... tation to more general con v ex cost functions Eg for nonzero cost functions in the in terv al use an additional pair of slac kv ariables. sub ject to f x i y y i f x i for ...
https://www.researchgate.net/publication/5606724_Robust_support_vector_regression_networks_for_function_approximation_with_outliers
Robust support vector regression networks for function approximation with outliers Article (PDF Available) in IEEE Transactions on Neural Networks 13(6):1322-30 · February 2002 with 350 Reads
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://medium.com/@george.drakos62/support-vector-machine-vs-logistic-regression-94cc2975433f
Aug 12, 2018 · Support Vector Machine (SVM) is an algorithm used for classification problems similar to Logistic Regression (LR). LR and SVM with linear Kernel generally perform comparably in practice.
https://www.quora.com/What-are-C-and-gamma-with-regards-to-a-support-vector-machine
C is the cost of misclassification as correctly stated by Dima. A large C gives you low bias and high variance. Low bias because you penalize the cost of missclasification a lot. A small C gives you higher bias and lower variance. Gamma is the par...
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