Missing Data Problems in Machine Learning

Name: Missing Data Problems in Machine Learning
Brand: VDM Verlag
SKU: 9783639212280
Price: 68.81 EUR
Availability: InStock

Outline and Contributions

Автор: Robin Parker

Език:

Корици: С меки корици

Издател: VDM Verlag

Наличност: Външен склад

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68.81 € 134.59 лв

Learning, inference, and prediction in the presence of missing data are pervasive problems in machin...

Информация за книгата

Автор

Robin Parker

Език

Английски език

Корици

Книга - С меки корици

Издадена

2010

страници

168

EAN

9783639212280

ISBN

3639212282

Enbook ID

06829144

Издател

VDM Verlag

Теглоt

254

Размери

152 x 229 x 10

Пълно описание

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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Missing Data Problems in Machine Learning

Outline and Contributions

Информация за книгата

Пълно описание

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