Abstract
An increasing number of data mining domains consider data that can be represented as permutations. Therefore, it is important to devise new methods to learn predictive models over datasets of permutations. However, maintaining probability distributions over the space of permutations is a hard task since there are n! permutations of n elements. The Fourier transform has been successfully generalized to functions over permutations. One of its main advantages in the context of probability distributions is that it compactly summarizes approximations to functions by discarding high order marginals information. In this paper, we present a method to learn a probability distribution that approximates the generating distribution of a given sample of permutations. In particular, this method learns the Fourier domain information representing this probability distribution.
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© 2011 Springer-Verlag Berlin Heidelberg
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Irurozki, E., Calvo, B., Lozano, J.A. (2011). Learning Probability Distributions over Permutations by Means of Fourier Coefficients. In: Butz, C., Lingras, P. (eds) Advances in Artificial Intelligence. Canadian AI 2011. Lecture Notes in Computer Science(), vol 6657. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21043-3_22
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DOI: https://doi.org/10.1007/978-3-642-21043-3_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21042-6
Online ISBN: 978-3-642-21043-3
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