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https://www.researchgate.net/publication/221079040_Selection_of_Meta-parameters_for_Support_Vector_Regression
2 Support Vector Regression and SVM Paramet er Selection In regression fo rmulation, the goal is to e stimate an un known conti nuous-value d function based on a f inite number of training sam ples.
https://experts.umn.edu/en/publications/selection-of-meta-parameters-for-support-vector-regression
/ Selection of meta-parameters for support vector regression. Artificial Neural Networks, ICANN 2002 - International Conference, Proceedings. Vol. 2415 LNCS Springer Verlag, 2002. pp. 687-693 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).Cited by: 83
https://link.springer.com/chapter/10.1007%2F3-540-46084-5_112
Aug 21, 2002 · Good generalization performance of the proposed parameter selection is demonstrated empirically using several low-dimensional and high-dimensional regression problems. In addition, we compare generalization performance of SVM regression (with proposed choiceε) with robust regression using ‘least-modulus’ loss function (ε=0).Cited by: 83
http://people.ece.umn.edu/users/cherkass/N2002-SI-SVM-13-whole.doc
We investigate practical selection of meta-parameters for SVM regression (that is, -insensitive zone and regularization parameter C). The proposed methodology advocates analytic parameter selection directly from the training data, rather than resampling approaches commonly used in SVM applications. ... Support Vector Regression and SVM ...
https://www.hindawi.com/journals/jcse/2017/3614790/
Support vector regression algorithm is widely used in fault diagnosis of rolling bearing. A new model parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function is proposed in this paper. We choose the mixed kernel function as the kernel function of support vector regression.Cited by: 5
http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/6330/pdf/imm6330.pdf
SVMs require the selection of two structural parameters, the penalty term that is applied to margin slack values and, in the case of Support Vector Regression (SVR), the tolerance threshold under which errors are not penalized. If, as usually done, Gaussian kernels are used, a third parameter, the kernel width has also to be selected.
https://www.sciencedirect.com/science/article/pii/S0893608003001692
We investigate practical selection of hyper-parameters for support vector machines (SVM) regression (that is, ε-insensitive zone and regularization parameter C).The proposed methodology advocates analytic parameter selection directly from the training data, rather than re-sampling approaches commonly used in SVM applications.Cited by: 1875
https://stackoverflow.com/questions/48033510/tuning-parameters-for-svm-regression
I am trying to create a SV Regression. I am generating the data from sinc function with some Gaussian noise. Now, in oder to find the best parameters to for RBF kernel, I am using GridSearchCV by running 5-fold cross validation.
https://www.researchgate.net/post/How_do_I_select_hyper_parameters_in_support_vector_regression
I want to use support vector regression to predict the future values in a time series. But how can I select the optimum value of hyper parameters like epsilon,C etc.
http://people.ece.umn.edu/users/cherkass/Regressionloss.doc
Practical Selection of SVM Parameters and Noise Estimation for SVM Regression, Neurocomputing (under review). Cherkassky, V., & Ma, Y.(2002). Selection of Meta-Parameters for Support Vector Regression, Proc. ICANN-2002 (to appear). Cherkassky, V.(2002). Model Complexity Control and Statistical Learning Theory, Natural Computing, 1, Kluwer, 109-133.
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