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＜電子ブック＞
Algorithmic Learning in a Random World / by Vladimir Vovk, Alexander Gammerman, Glenn Shafer

版	2nd ed. 2022.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2022
本文言語	英語
大きさ	XXVI, 476 p. 83 illus., 58 illus. in color : online resource
著者標目	*Vovk, Vladimir author Gammerman, Alexander author Shafer, Glenn author SpringerLink (Online service)
件　名	LCSH:Machine learning LCSH:Computer science -- Mathematics  全ての件名で検索 LCSH:Mathematical statistics LCSH:Algorithms LCSH:Artificial intelligence FREE:Machine Learning FREE:Probability and Statistics in Computer Science FREE:Design and Analysis of Algorithms FREE:Artificial Intelligence
一般注記	1. Introduction -- Part I Set prediction -- 2. Conformal prediction: general case and regression -- 3. Conformal prediction: classification and general case -- 4. Modifications of conformal predictors -- Part II Probabilistic prediction -- 5. Impossibility results -- 6. Probabilistic classification: Venn predictors -- 7. Probabilistic regression: conformal predictive systems -- Part III Testing randomness -- 8. Testing exchangeability -- 9. Efficiency of conformal testing -- 10. Non-conformal shortcut -- Part IV Online compression modelling -- 11. Generalized conformal prediction -- 12. Generalized Venn prediction and hypergraphical models -- 13. Contrasts and perspectives 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 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. Algorithmic Learning in a Random World contains, in addition to proofs of validity, results about the efficiency of conformal predictors. The only assumption required for validity is that of "randomness" (the prediction algorithm is presented with independent and identically distributed examples); in later chapters, even the assumption of randomness is significantly relaxed. Interesting results about efficiency are established both under randomness and under stronger assumptions. Since publication of the First Edition in 2005 conformal prediction has found numerous applications in medicine and industry, and is becoming a popular machine-learning technique. This Second Edition contains three new chapters. One is about conformal predictive distributions, which are more informative than the set predictions produced by standard conformal predictors. Another is about the efficiency of ways of testing the assumption of randomness based on conformal prediction. The third new chapter harnesses conformal testing procedures for protecting machine-learning algorithms against changes in the distribution of the data. In addition, the existing chapters have been revised, updated, and expanded HTTP:URL=https://doi.org/10.1007/978-3-031-06649-8

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