The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Geomodeling
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A tutorial on support vector regression
Statistics and Computing
Training algorithms for fuzzy support vector machines with noisy data
Pattern Recognition Letters
Intrinsic Kriging and prior information: Research Articles
Applied Stochastic Models in Business and Industry - Statistical Learning
An information-geometrical approach to constructing kernel in support vector regression machines
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
IEEE Transactions on Signal Processing
Bayesian support vector regression using a unified loss function
IEEE Transactions on Neural Networks
Simple estimate of the width in Gaussian kernel with adaptive scaling technique
Applied Soft Computing
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Support vector regression (SVR) is a powerful learning technique in the framework of statistical learning theory, while Kriging is a well-entrenched prediction method traditionally used in the spatial statistics field. However, the two techniques share the same framework of reproducing kernel Hilbert space. In this paper, we first review the formulations of SILF-SVR where soft insensitive loss function is utilized and ordinary Kriging, and then prove the equivalence between the two techniques under the assumption that the kernel function is substituted by covariance function.