Prediction and modeling with partial dependencies

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Abstract

The author consider a binary classification problem with a feature vector of high dimensionality. Spam mail filters are a popular example hereof. A Bayesian approach requires us to estimate the probability of a feature vector given the class of the object. Due to the size of the feature vector this is an unfeasible task. A useful approach is to split the feature space into several (conditionally) independent subspaces. This results in a new problem, namely how to find the ldquobestrdquo subdivision. In this paper the author consider a weighing approach that will perform (asymptotically) as good as the best subdivision and still has a manageable complexity.
Original languageEnglish
Title of host publicationInformation Theory and Applications Workshop, 2008 , 3rd ,27 January -1 February 2008, San Diego, U.S.A.
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages1-11
ISBN (Print)978-1-4244-2670-6
DOIs
Publication statusPublished - 2008

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  • Cite this

    Tjalkens, T. J. (2008). Prediction and modeling with partial dependencies. In Information Theory and Applications Workshop, 2008 , 3rd ,27 January -1 February 2008, San Diego, U.S.A. (pp. 1-11). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ITA.2008.4601082