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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Missing Data Problems in Machine Learning

Missing Data Problems in Machine Learning

Parker, Robin

Paperback

Programming

ISBN10: 3639212282
ISBN13: 9783639212280
Publisher: Blues Kids Of Amer
Published: Jun 7 2010
Pages: 168
Weight: 0.56
Height: 0.39 Width: 6.00 Depth: 9.00
Language: English
Learning, inference, and prediction in the presence of missing data are pervasive problems in machine learning and statistical data analysis. This thesis focuses on the problems of collaborative prediction with non-random missing data and classification with missing features. We begin by presenting and elaborating on the theory of missing data due to Little and Rubin. We place a particular emphasis on the missing at random assumption in the multivariate setting with arbitrary patterns of missing data. We derive inference and prediction methods in the presence of random missing data for a variety of probabilistic models including finite mixture models, Dirichlet process mixture models, and factor analysis.

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