Sparse matrices in matlab: design and implementation
SIAM Journal on Matrix Analysis and Applications
Iterative methods for solving linear systems
Iterative methods for solving linear systems
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Classes of kernels for machine learning: a statistics perspective
The Journal of Machine Learning Research
Online Sparse Matrix Gaussian Process Regression and Vision Applications
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
The GCS kernel for SVM-based image recognition
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
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In this paper we investigate the use of compactly supported RBF kernels for nonlinear function estimation with LS-SVMs. The choice of compact kernels, recently proposed by Genton, may lead to computational improvements and memory reduction. Examples, however, illustrate that compactly supported RBF kernels may lead to severe loss in generalization performance for some applications, e.g. in chaotic time-series prediction. As a result, the usefulness of such kernels may be much more application dependent than the use of the RBF kernel.