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10

ICML
2005
IEEE

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A practical generalization of Fourier-based learning

14 years 5 months ago

A practical generalization of Fourier-based learning

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This paper presents a search algorithm for finding functions that are highly correlated with an arbitrary set of data. The functions found by the search can be used to approximate the unknown function that generated the data. A special case of this approach is a method for learning Fourier representations. Empirical results demonstrate that on typical real-world problems the most highly correlated functions can be found very quickly, while combinations of these functions provide good approximations of the unknown function.

Adam Drake, Dan Ventura

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Correlated Functions | ICML 2005 | Machine Learning | Typical Real-world Problems | Unknown Function |

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Added	17 Nov 2009
Updated	17 Nov 2009
Type	Conference
Year	2005
Where	ICML
Authors	Adam Drake, Dan Ventura

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