The nature of statistical learning theory
The nature of statistical learning theory
Matrix computations (3rd ed.)
Making large-scale support vector machine learning practical
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Some Properties of Regularized Kernel Methods
The Journal of Machine Learning Research
Learning from Examples as an Inverse Problem
The Journal of Machine Learning Research
On regularization algorithms in learning theory
Journal of Complexity
Scikit-learn: Machine Learning in Python
The Journal of Machine Learning Research
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In several supervised learning applications, it happens that reconstruction methods have to be applied repeatedly before being able to achieve the final solution. In these situations, the availability of learning algorithms able to provide effective predictors in a very short time may lead to remarkable improvements in the overall computational requirement. In this paper we consider the kernel ridge regression problem and we look for solutions given by a linear combination of kernel functions plus a constant term. In particular, we show that the unknown coefficients of the linear combination and the constant term can be obtained very fastly by applying specific regularization algorithms directly to the linear system arising from the Empirical Risk Minimization problem. From the numerical experiments carried out on benchmark datasets, we observed that in some cases the same results achieved after hours of calculations can be obtained in few seconds, thus showing that these strategies are very well-suited for time-consuming applications.