The nature of statistical learning theory
The nature of statistical learning theory
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Support vector machines for dynamic reconstruction of a chaotic system
Advances in kernel methods
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Linear Dependency between epsilon and the Input Noise in epsilon-Support Vector Regression
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Integrating support vector machines and neural networks
Neural Networks
Bounded influence support vector regression for robust single-model estimation
IEEE Transactions on Neural Networks
Robotics and Computer-Integrated Manufacturing
A generalized learning based framework for fast brain image registration
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
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We propose practical recommendations for selecting metaparameters for SVM regression (that is, 驴 -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. Good generalization performance of the proposed parameter selection is demonstrated empirically using several lowdimensional and high-dimensional regression problems. In addition, we compare generalization performance of SVM regression (with proposed choice驴) with robust regression using 'least-modulus' loss function (驴 =0). These comparisons indicate superior generalization performance of SVM regression.