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Biographie et livres de Vladimir Vovk

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Vladimir Vovk is Professor of Computer Science at Royal Holloway, University of London; he also heads the Computer Learning Research Centre. His research interests include machine learning; predictive and Kolmogorov complexity, randomness, and information; the foundations of probability and statistics. He has published numerous research papers in these fields and two books: "Probability and finance: It's only a game" (with Glenn Shafer, Wiley, New York, 2001; Japanese translation: Iwanami
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Téléchargez le livre :  Algorithmic Learning in a Random World
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Algorithmic Learning in a Random World

Alexander Gammerman , Glenn Shafer , Vladimir Vovk


Springer

2022-12-13

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This book is about conformal prediction, an approach to prediction that originated in machine learning in the late 1990s. The main feature of conformal prediction is the principled treatment of the reliability of predictions. The prediction...

168,79

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Télécharger le livre :  Game-Theoretic Foundations for Probability and Finance
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Game-theoretic probability and finance come of age Glenn Shafer and Vladimir Vovk’s Probability and Finance, published in 2001, showed that perfect-information games can be used to define mathematical probability. Based on fifteen years of further research,...

Editeur : Wiley
Parution : 2019-05-08
Collection : Wiley Series in Probability and Statistics
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117,05
Télécharger le livre :  Measures of Complexity
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This book brings together historical notes, reviews of research developments, fresh ideas on how to make VC (Vapnik–Chervonenkis) guarantees tighter, and new technical contributions in the areas of machine learning, statistical inference, classification, algorithmic...

Editeur : Springer
Parution : 2015-09-03

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94,94
Télécharger le livre :  Empirical Inference
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This book honours the outstanding contributions of Vladimir Vapnik, a rare example of a scientist for whom the following statements hold true simultaneously: his work led to the inception of a new field of research, the theory of statistical learning and empirical...

Editeur : Springer
Parution : 2013-12-11

ePub

52,74
Télécharger le livre :  Algorithmic Learning in a Random World
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Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. Based on these approximations, a new set of machine learning algorithms have been...

Editeur : Springer
Parution : 2005-12-05

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168,79