Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Similarity metric learning for a variable-kernel classifier
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Regularization with a pruning prior
Neural Networks
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Evaluation of gaussian processes and other methods for non-linear regression
Evaluation of gaussian processes and other methods for non-linear regression
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Kernel smoothing is a widely used non-parametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this contribution, we propose an algorithm that adapts the input metric used in multivariate regression by minimising a cross-validation estimate of the generalisation error. This allows to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms the standard approach. Finally, we benchmark the method using the DELVE environment.