Algorithmic Learning in a Random World

Vladimir Vovk (), Alexander Gammerman () and Glenn Shafer ()
Additional contact information
Vladimir Vovk: University of London, Royal Holloway
Alexander Gammerman: University of London, Royal Holloway
Glenn Shafer: Rutgers University

in Springer Books from Springer

Date: 2022
Edition: 2nd ed. 2022
ISBN: 978-3-031-06649-8
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Chapters in this book:

Ch Chapter 1 Introduction

Vladimir Vovk, Alexander Gammerman and Glenn Shafer

Ch Chapter 10 Non-conformal Shortcut

Vladimir Vovk, Alexander Gammerman and Glenn Shafer

Ch Chapter 11 Generalized Conformal Prediction

Vladimir Vovk, Alexander Gammerman and Glenn Shafer

Ch Chapter 12 Generalized Venn Prediction and Hypergraphical Models

Vladimir Vovk, Alexander Gammerman and Glenn Shafer

Ch Chapter 13 Contrasts and Perspectives

Vladimir Vovk, Alexander Gammerman and Glenn Shafer

Ch Chapter 2 Conformal Prediction: General Case and Regression

Vladimir Vovk, Alexander Gammerman and Glenn Shafer

Ch Chapter 3 Conformal Prediction: Classification and General Case

Vladimir Vovk, Alexander Gammerman and Glenn Shafer

Ch Chapter 4 Modifications of Conformal Predictors

Vladimir Vovk, Alexander Gammerman and Glenn Shafer

Ch Chapter 5 Impossibility Results

Vladimir Vovk, Alexander Gammerman and Glenn Shafer

Ch Chapter 6 Probabilistic Classification: Venn Predictors

Vladimir Vovk, Alexander Gammerman and Glenn Shafer

Ch Chapter 7 Probabilistic Regression: Conformal Predictive Systems

Vladimir Vovk, Alexander Gammerman and Glenn Shafer

Ch Chapter 8 Testing Exchangeability

Vladimir Vovk, Alexander Gammerman and Glenn Shafer

Ch Chapter 9 Efficiency of Conformal Testing

Vladimir Vovk, Alexander Gammerman and Glenn Shafer

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DOI: 10.1007/978-3-031-06649-8

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