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Aggiungi al carrelloCondizione: Very Good. Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc.
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Aggiungi al carrellohardcover. Condizione: New. In shrink wrap. Looks like an interesting title!
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EUR 177,46
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EUR 177,46
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EUR 180,21
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EUR 180,21
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EUR 180,21
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Editore: Springer International Publishing, 2022
ISBN 10: 3031066480 ISBN 13: 9783031066481
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 181,89
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Aggiungi al carrelloBuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 Worldcontains, 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.
Editore: Springer International Publishing, 2023
ISBN 10: 3031066510 ISBN 13: 9783031066511
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 181,89
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 Worldcontains, 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.
EUR 196,38
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
EUR 196,61
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EUR 196,24
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EUR 196,30
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EUR 196,40
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EUR 197,27
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
EUR 208,47
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EUR 208,47
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EUR 208,47
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EUR 210,92
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EUR 203,69
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Aggiungi al carrelloCondizione: New. About conformal prediction, which is a valuable new method of machine learningConformal predictors are among the most accurate methods of machine learning, and unlike other state-of-the-art methods, they provide information about their own accurac.
EUR 233,62
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Aggiungi al carrelloCondizione: New. 2nd ed. 2022 edition NO-PA16APR2015-KAP.
Editore: Springer-Verlag New York Inc., 2005
ISBN 10: 0387001522 ISBN 13: 9780387001524
Lingua: Inglese
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
EUR 234,09
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Aggiungi al carrelloCondizione: New. A scientific monograph that develops algorithmic foundations in machine learning theory. It is intended for researchers and postgraduates in CS, statistics, and AI. Num Pages: 340 pages, 62 black & white illustrations, biography. BIC Classification: UMB; UYQM. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 234 x 156 x 20. Weight in Grams: 1450. . 2005. Hardback. . . . .
EUR 184,72
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