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International EditionALGORITHMIC LEARNING IN A RANDOM WORLD
Language: English
Published by New Age International Publisher, 2007
ISBN 10: 8181286561 ISBN 13: 9788181286567
Condition: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.
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Algorithmic Learning in a Random World
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
US$ 60.70
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US$ 58.49
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Hardcover. Condition: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.
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Condition: Very Good. Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.
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hardcover. Condition: New. In shrink wrap. Looks like an interesting title!
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Condition: New.
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Condition: New. pp. 344.
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Algorithmic Learning in a Random World (Hardcover)
Language: English
Published by Springer International Publishing AG, Cham, 2022
ISBN 10: 3031066480 ISBN 13: 9783031066481
Hardcover. Condition: 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
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
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Algorithmic Learning in a Random World
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Algorithmic Learning in a Random World
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Condition: As New. Unread book in perfect condition.
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Condition: As New. Unread book in perfect condition.
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Hardcover. Condition: Gut. 340 pp. Cover discolored at the spine, otherwise well preserved copy 350 Sprache: Englisch Gewicht in Gramm: 808.
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Algorithmic Learning in a Random World
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Algorithmic Learning In A Random World
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Algorithmic Learning in a Random World
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Taschenbuch. Condition: 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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Condition: New. 2nd ed. 2022 edition NO-PA16APR2015-KAP.
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Algorithmic Learning In A Random World
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
Seller rating 5 out of 5 stars
Condition: New.
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Algorithmic Learning in a Random World
Language: English
Published by Springer International Publishing, 2023
ISBN 10: 3031066510 ISBN 13: 9783031066511
Taschenbuch. Condition: 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
Language: English
Published by Springer International Publishing, 2022
ISBN 10: 3031066480 ISBN 13: 9783031066481
Buch. Condition: 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 (Hardcover)
Language: English
Published by Springer International Publishing AG, Cham, 2022
ISBN 10: 3031066480 ISBN 13: 9783031066481
Hardcover. Condition: 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.
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Algorithmic Learning in a Random World
Language: English
Published by Springer-Nature New York Inc, 2023
ISBN 10: 3031066510 ISBN 13: 9783031066511
US$ 323.71
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Add to basketPaperback. Condition: Brand New. 2nd edition. 502 pages. 9.25x6.10x1.01 inches. In Stock.
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US$ 326.10
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Add to basketHardcover. Condition: Brand New. 2nd edition. 502 pages. 9.25x6.10x1.34 inches. In Stock.
