Regularization Theory for Ill-posed Problems
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Published By DE GRUYTER

9783110286496

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Regularization Theory for Ill-posed Problems ◽

10.1515/9783110286496.277 ◽

2013 ◽

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Chapter 5. Meta-learning approach to regularization – case study: blood glucose prediction

Regularization Theory for Ill-posed Problems ◽

10.1515/9783110286496.255 ◽

2013 ◽

Keyword(s):

Blood Glucose ◽

Learning Approach ◽

Meta Learning ◽

Glucose Prediction

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Chapter 3. Multiparameter regularization

Regularization Theory for Ill-posed Problems ◽

10.1515/9783110286496.163 ◽

2013 ◽

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Frontmatter

Regularization Theory for Ill-posed Problems ◽

10.1515/9783110286496.fm ◽

2013 ◽

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Chapter 1. An introduction using classical examples

Regularization Theory for Ill-posed Problems ◽

10.1515/9783110286496.1 ◽

2013 ◽

pp. 1-46

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Preface

Regularization Theory for Ill-posed Problems ◽

10.1515/9783110286496.vii ◽

2013 ◽

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Contents

Regularization Theory for Ill-posed Problems ◽

10.1515/9783110286496.toc ◽

2013 ◽

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Index

Regularization Theory for Ill-posed Problems ◽

10.1515/9783110286496.289 ◽

2013 ◽

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Chapter 2. Basics of single parameter regularization schemes

Regularization Theory for Ill-posed Problems ◽

10.1515/9783110286496.47 ◽

2013 ◽

Keyword(s):

Single Parameter ◽

Parameter Regularization

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Chapter 4. Regularization algorithms in learning theory

Regularization Theory for Ill-posed Problems ◽

10.1515/9783110286496.203 ◽

2013 ◽

Keyword(s):

Learning Theory

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