high-order correlations, feature selection, learning theory
Many learning algorithms attempt, either explicitly or implicitly, to discover useful high-order features. When considering all possible functions that could be encountered, no particular type of high-order feature should be more useful than any other. However, this paper presents arguments and empirical results that suggest that for the learning problems typically encountered in practice, some high-order features may be more useful than others.
Original Publication Citation
Adam Drake and Dan Ventura, "Comparing High-Order Boolean Features", Proceedings of the Joint Conference on Information Sciences, pp. 428-431, July 25.
BYU ScholarsArchive Citation
Drake, Adam and Ventura, Dan A., "Comparing High-Order Boolean Features" (2005). Faculty Publications. 364.
Physical and Mathematical Sciences
© 2005 Dan Ventura et al.
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