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Study Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques
Posted on 2010-03-16
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More Tshilidzi Marwala "Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques" In recent years, the issue of missing data imputation has been extensively explored in information engineering. Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques presents methods and technologies in estimation of missing values given the observed data. Providing a defining body of research valuable to those involved in the field of study, this book covers techniques such as radial basis functions, support vector machines, and principal component analysis. Table of Contents: Chapter I: Introduction to Missing Data Chapter II: Estimation of Missing Data Using Neural Networks and Genetic Algorithms Chapter III: A Hybrid Approach to Missing Data: Bayesian Neural Networks, Principal Component Analysis and Genetic Algorithms Chapter IV: Maximum Expectation Algorithms for Missing Data Estimation Chapter V: Missing Data Estimation Using Rough Sets Chapter VI: Support Vector Regression for Missing Data Estimation Chapter VII: Committee of Networks for Estimating Missing Data Chapter VIII: Online Approaches to Missing Data Estimation Chapter IX: Missing Data Approaches to Classification Chapter X: Optimization Methods for Estimation of Missing Data Chapter XI: Estimation of Missing Data Using Neural Networks and Decision Trees Chapter XII: Control of Biomedical System Using Missing Data Approaches Chapter XIII: Emerging Missing Data Estimation Problems: Heteroskedasticity; Dynamic Programming and Impact of Missing Data To start download free click HERE: >>> Download many interesting free eBooks HERE <<< No another mirrors, please! >>> Read RULES
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