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" Approximations of Bayes Classifiers for Statistical Learning of Clusters "
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BL
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| Record Number
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959765
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| Doc. No
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b714135
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| Main Entry
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Ekdahl, Magnus,1979-
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| Title & Author
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Approximations of Bayes Classifiers for Statistical Learning of Clusters
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| Publication Statement
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Matematiska institutionen,, 2006
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| Series Statement
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Linköping Studies in Science and Technology. Thesis,; 1230
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| Page. NO
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86
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| ISBN
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9185497215
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| Notes
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<p>Report code: LiU-TEK-LIC 2006:11.</p>
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| Abstract
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It is rarely possible to use an optimal classifier. Often the classifier used for a specific problem is an approximation of the optimal classifier. Methods are presented for evaluating the performance of an approximation in the model class of Bayesian Networks. Specifically for the approximation of class conditional independence a bound for the performance is sharpened. The class conditional independence approximation is connected to the minimum description length principle (MDL), which is connected to Jeffreys' prior through commonly used assumptions. One algorithm for unsupervised classification is presented and compared against other unsupervised classifiers on three data sets.
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| Subject
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Applied mathematics.
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Matematik.
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MATEMATIK.
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Matematisk statistik.
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Mathematical statistics.
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Mathematics.
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MATHEMATICS.
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Natural Sciences.
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Naturvetenskap.
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Probability Theory and Statistics.
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Sannolikhetsteori och statistik.
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Tillämpad matematik.
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| Added Entry
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Corander, Jukka.
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Koski, Timo.
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Linköpings universitet, Matematiska institutionen.
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Linköpings universitet, Tekniska högskolan.
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