Optimal $γ$ and $C$ for $ε$-Support Vector Regression with RBF Kernels
The objective of this study is to investigate the efficient determination of $C$ and $\gamma$ for Support Vector Regression with RBF or mahalanobis kernel based on numerical and statistician considerations, which indicates the connection between $C$ and kernels and demonstrates that the deviation of geometric distance of neighbour observation in mapped space effects the predict accuracy of $\epsilon$-SVR. We determinate the arrange of $\gamma$ & $C$ and propose our method to choose their best values.
Code (0)
등록된 구현이 없습니다.
Tasks
regressionSimilar Papers 제목 키워드 기반
Generalized support vector regression: duality and tensor-kernel representation
In this paper we study the variational problem associated to support vector regression in Banach function spaces. Using the Fenchel-Rockafellar duality theory, we give explicit formulation of the dual problem as well as …
regressionOptimal learning rates for least squares SVMs using Gaussian kernels
We prove a new oracle inequality for support vector machines with Gaussian RBF kernels solving the regularized least squares regression problem. To this end, we apply the modulus of smoothness. With the help of the new o…
regressionSymmetric and antisymmetric properties of solutions to kernel-based machine learning problems
A particularly interesting instance of supervised learning with kernels is when each training example is associated with two objects, as in pairwise classification (Brunner et al., 2012), and in supervised learning of pr…
BIG-bench Machine LearningBinary ClassificationGeneral ClassificationLearning Rates for Kernel-Based Expectile Regression
Conditional expectiles are becoming an increasingly important tool in finance as well as in other areas of applications. We analyse a support vector machine type approach for estimating conditional expectiles and establi…
regressionLearning rates for classification with Gaussian kernels
This paper aims at refined error analysis for binary classification using support vector machine (SVM) with Gaussian kernel and convex loss. Our first result shows that for some loss functions such as the truncated quadr…
Binary ClassificationClassificationGeneral Classificationregression