Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques > Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques > Chapter XII: Control of Biomedical System Using Missing Data Approaches

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Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques

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Table of Contents

Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques

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 Analysisand 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

INTRODUCTION

THEORETICAL BACKGROUND

METHODOLOGIES FOLLOWED

TESTING THE PROPOSED PROCEDURE

REFERENCES

Chapter XIII: Emerging Missing DataEstimation Problems: Heteroskedasticity; Dynamic Programming and Impact of Missing Data

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Index

Marwala Control of Biomedical System Using Missing Data Approaches Chapter XII ABSTRACT Neural.networks.are.used.in.this.chapter.for.classifying.the.HIV.status.of.individuals.based.on.socio- economic.and.demographic.characteristics..The.trained.network.is.then.used.to.create.an.error.equa-

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