Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Methodology for long-term prediction of time series
Neurocomputing
Minimising the delta test for variable selection in regression problems
International Journal of High Performance Systems Architecture
The curse of dimensionality in data mining and time series prediction
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Linear feature selection in texture analysis - A PLS based method
Machine Vision and Applications
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In this paper, the effect of dimensionality on the supervised learning of infinitely differentiable regression functions is analyzed. By invoking the Van Trees lower bound, we prove lower bounds on the generalization error with respect to the number of samples and the dimensionality of the input space both in a linear and non-linear context. It is shown that in non-linear problems without prior knowledge, the curse of dimensionality is a serious problem. At the same time, we speculate counter-intuitively that sometimes supervised learning becomes plausible in the asymptotic limit of infinite dimensionality.