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Название: Algorithmic Learning in a Random World, 2nd Edition
Автор: Vladimir Vovk, Alexander Gammerman, Glenn Shafer
Издательство: Springer
Год: 2022
Страниц: 490
Язык: английский
Формат: pdf (true), epub
Размер: 29.3 MB
This book is about conformal prediction, an approach to prediction that originated in Machine Learning. The main feature of conformal prediction is the principled treatment of the reliability of predictions. The prediction algorithms described ― conformal predictors ― are provably valid in the sense that they evaluate the reliability of their own predictions in a way that is neither over-pessimistic nor over-optimistic (the latter being especially dangerous). The approach is still flexible enough to incorporate most of the existing powerful methods of Machine Learning. The book covers both key conformal predictors and the mathematical analysis of their properties.
Автор: Vladimir Vovk, Alexander Gammerman, Glenn Shafer
Издательство: Springer
Год: 2022
Страниц: 490
Язык: английский
Формат: pdf (true), epub
Размер: 29.3 MB
This book is about conformal prediction, an approach to prediction that originated in Machine Learning. The main feature of conformal prediction is the principled treatment of the reliability of predictions. The prediction algorithms described ― conformal predictors ― are provably valid in the sense that they evaluate the reliability of their own predictions in a way that is neither over-pessimistic nor over-optimistic (the latter being especially dangerous). The approach is still flexible enough to incorporate most of the existing powerful methods of Machine Learning. The book covers both key conformal predictors and the mathematical analysis of their properties.