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Algorithmic Learning in a Random World
Da: Chiron Media, Wallingford, Regno Unito
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EUR 50,22
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Spedito da Regno Unito a U.S.A.Quantità: 9 disponibili
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Algorithmic Learning in a Random World
Da: Books From California, Simi Valley, CA, U.S.A.
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paperback. Condizione: Fine.
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Algorithmic Learning in a Random World
Da: ThriftBooks-Dallas, Dallas, TX, U.S.A.
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Hardcover. Condizione: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.
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Algorithmic Learning in a Random World
Da: BennettBooksLtd, San Diego, NV, U.S.A.
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hardcover. Condizione: New. In shrink wrap. Looks like an interesting title!
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Algorithmic Learning in a Random World (Hardcover)
Lingua: Inglese
Editore: Springer International Publishing AG, Cham, 2022
ISBN 10: 3031066480 ISBN 13: 9783031066481
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
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Hardcover. Condizione: new. Hardcover. 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. This book is about conformal prediction, an approach to prediction that originated in machine learning in the late 1990s. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Algorithmic Learning in a Random World
Da: GreatBookPrices, Columbia, MD, U.S.A.
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Algorithmic Learning in a Random World
Da: Revaluation Books, Exeter, Regno Unito
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EUR 164,94
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Aggiungi al carrelloPaperback. Condizione: Brand New. 344 pages. 9.25x6.10x0.77 inches. In Stock.
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Algorithmic Learning In A Random World
Da: GreatBookPrices, Columbia, MD, U.S.A.
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Condizione: New.
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Algorithmic Learning in a Random World
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
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Condizione: New.
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Algorithmic Learning In A Random World
Da: GreatBookPricesUK, Woodford Green, Regno Unito
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EUR 173,12
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Algorithmic Learning in a Random World
Da: GreatBookPrices, Columbia, MD, U.S.A.
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Condizione: As New. Unread book in perfect condition.
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Algorithmic Learning in a Random World
Da: GreatBookPrices, Columbia, MD, U.S.A.
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Condizione: As New. Unread book in perfect condition.
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Algorithmic Learning In A Random World
Da: GreatBookPrices, Columbia, MD, U.S.A.
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Condizione: As New. Unread book in perfect condition.
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Algorithmic Learning in a Random World
Da: GreatBookPricesUK, Woodford Green, Regno Unito
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EUR 184,66
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Algorithmic Learning in a Random World
Da: GreatBookPricesUK, Woodford Green, Regno Unito
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EUR 188,85
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Algorithmic Learning in a Random World
Da: GreatBookPrices, Columbia, MD, U.S.A.
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Algorithmic Learning in a Random World
Da: California Books, Miami, FL, U.S.A.
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EUR 208,42
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Algorithmic Learning in a Random World
Da: California Books, Miami, FL, U.S.A.
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EUR 208,42
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Algorithmic Learning in a Random World
Da: California Books, Miami, FL, U.S.A.
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EUR 208,42
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Algorithmic Learning in a Random World
Da: GreatBookPricesUK, Woodford Green, Regno Unito
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EUR 195,04
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Algorithmic Learning in a Random World
Da: GreatBookPricesUK, Woodford Green, Regno Unito
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EUR 195,04
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Algorithmic Learning In A Random World
Da: GreatBookPricesUK, Woodford Green, Regno Unito
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EUR 195,04
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Algorithmic Learning in a Random World
Da: Books Puddle, New York, NY, U.S.A.
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Condizione: New.
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Algorithmic Learning in a Random World
Da: Books Puddle, New York, NY, U.S.A.
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Condizione: New. 2nd ed. 2022 edition NO-PA16APR2015-KAP.
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EUR 159,50
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Algorithmic Learning in a Random World | Vladimir Vovk (u. a.) | Taschenbuch | xxvi | Englisch | 2023 | Springer | EAN 9783031066511 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Algorithmic Learning in a Random World
Lingua: Inglese
Editore: Springer International Publishing, 2023
ISBN 10: 3031066510 ISBN 13: 9783031066511
EUR 181,89
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Spedito da Germania a U.S.A.Quantità: 1 disponibili
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.
-
Algorithmic Learning in a Random World
Lingua: Inglese
Editore: Springer International Publishing, 2022
ISBN 10: 3031066480 ISBN 13: 9783031066481
EUR 181,89
EUR 64,58 shipping
Spedito da Germania a U.S.A.Quantità: 1 disponibili
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.
-
EUR 206,48
EUR 48,99 shipping
Spedito da Germania a U.S.A.Quantità: Più di 20 disponibili
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.
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Algorithmic Learning in a Random World
Da: Revaluation Books, Exeter, Regno Unito
Valutazione del venditore 5 su 5 stelle
EUR 257,57
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Spedito da Regno Unito a U.S.A.Quantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: Brand New. 344 pages. 9.25x6.10x0.77 inches. In Stock.
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Algorithmic Learning in a Random World (Hardcover)
Lingua: Inglese
Editore: Springer International Publishing AG, Cham, 2022
ISBN 10: 3031066480 ISBN 13: 9783031066481
Da: AussieBookSeller, Truganina, VIC, Australia
Valutazione del venditore 5 su 5 stelle
EUR 250,11
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Spedito da Australia a U.S.A.Quantità: 1 disponibili
Aggiungi al carrelloHardcover. Condizione: new. Hardcover. 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. This book is about conformal prediction, an approach to prediction that originated in machine learning in the late 1990s. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
