Missing Data Problems in Machine Learning: Outline and Contributions - Robin Parker
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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 m ... Full description
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Description
More Information
| Author | Robin Parker |
|---|---|
| Publisher | VDM Verlag Dr. Müller |
| Release year | 2010 |
| Cover type | Softcover |
| EAN | 9783639212280 |