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Braverman Readings in Machine Learning. Key Ideas from Inception to Current State

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Cover of 'Braverman Readings in Machine Learning. Key Ideas from Inception to Current State'

Table of Contents

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    Book Overview
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    Chapter 1 Potential Functions for Signals and Symbolic Sequences
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    Chapter 2 Braverman’s Spectrum and Matrix Diagonalization Versus iK-Means: A Unified Framework for Clustering
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    Chapter 3 Compactness Hypothesis, Potential Functions, and Rectifying Linear Space in Machine Learning
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    Chapter 4 Conformal Predictive Distributions with Kernels
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    Chapter 5 On the Concept of Compositional Complexity
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    Chapter 6 On the Choice of a Kernel Function in Symmetric Spaces
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    Chapter 7 Causality Modeling and Statistical Generative Mechanisms
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    Chapter 8 One-Class Semi-supervised Learning
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    Chapter 9 Prediction of Drug Efficiency by Transferring Gene Expression Data from Cell Lines to Cancer Patients
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    Chapter 10 On One Approach to Robot Motion Planning
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    Chapter 11 Geometrical Insights for Implicit Generative Modeling
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    Chapter 12 Deep Learning in the Natural Sciences: Applications to Physics
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    Chapter 13 From Reinforcement Learning to Deep Reinforcement Learning: An Overview
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    Chapter 14 A Man of Unlimited Capabilities (in Memory of E. M. Braverman)
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    Chapter 15 Braverman and His Theory of Disequilibrium Economics
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    Chapter 16 Misha Braverman: My Mentor and My Model
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    Chapter 17 List of Braverman’s Papers Published in the “Avtomatika i telemekhanika” Journal, Moscow, Russia, and Translated to English as “Automation and Remote Control” Journal
Attention for Chapter 4: Conformal Predictive Distributions with Kernels
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Chapter title
Conformal Predictive Distributions with Kernels
Chapter number 4
Book title
Braverman Readings in Machine Learning. Key Ideas from Inception to Current State
Published by
Springer, Cham, January 2018
DOI 10.1007/978-3-319-99492-5_4
Book ISBNs
978-3-31-999491-8, 978-3-31-999492-5
Authors

Vladimir Vovk, Ilia Nouretdinov, Valery Manokhin, Alex Gammerman, Vovk, Vladimir, Nouretdinov, Ilia, Manokhin, Valery, Gammerman, Alex

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 40%
Researcher 3 20%
Student > Doctoral Student 2 13%
Student > Master 1 7%
Unknown 3 20%
Readers by discipline Count As %
Computer Science 4 27%
Mathematics 3 20%
Medicine and Dentistry 2 13%
Pharmacology, Toxicology and Pharmaceutical Science 1 7%
Chemistry 1 7%
Other 1 7%
Unknown 3 20%