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Cai, Di; McCluskey, Thomas L. (2012)
Publisher: Journal of Computing
Languages: English
Types: Article
Subjects: Q1
The ability to formally analyze and automatically measure statistical dependence of terms is a core problem in many areas of science. One of the commonly used tools for this is the expected mutual information (MI) measure. However, it seems\ud that MI methods have not achieved their potential. The main problem in using MI of terms is to obtain actual probability distributions estimated from training data, as the true distributions are invariably not known. This study focuses on the problem and proposes a novel but simple method for estimating probability distributions. Estimation functions are introduced; mathematical meaning of the functions is interpreted and the verification conditions are discussed. Examples are provided to illustrate the possibility of failure of applying the method if the verification conditions are not satisfied. An extension of the method is considered.
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    • M. Ait Kerrou m, A. H am mou ch, and D. Aboutajd ine, “Textu ral featu re selection by joint m u tual inform ation based on gau ssian m ixtu re m od el for m u ltisp ectral im age classification,” Pattern Recognition Letters, vol 3, no. 10, pp . 1168-1174, 2010.
    • [2] A. E. Akad i, A.E. Abd eljalil El Ou ard ighi, and D. Abou tajd ine, “A pow erfu l featu re selection ap p roach based on m u tu al inform ation,” International Journal of Computer Science and N etwork Security, vol. 8, p p . 116-121, 2008.
    • for Information Science, vol. 16, no. 1, p p. 22-29, 1990.
    • [6] H . Fang and C. X. Zhai, “Semantic term m atching in axiom atic [7] S. Gau ch, J. Wang, and S. M. Rachakond a, “A corp u s analysis [10] H .-W. Liu , J.-G. Su n, L. Liu , and H .-J. Zhang, “Featu re selection [11] R. M. Losee, Jr., “Term d ep end ence: A basis for Lu hn and Zip f [13] R. Mand ala, T. Toku naga, and H . Tanaka, “Qu ery exp ansion [17] G. Wang, F.H . Lochovsky, and Q. Yang, “Featu re selection w ith m u tu al
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