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Algorithmic Learning Random World by Vovk Vladimir (50 results)
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Algorithmic Learning in a Random World
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
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Condition: good. The item shows wear from consistent use, but it remains in good condition and works perfectly. All pages and cover are intact including the dust cover, if applicable . Spine may show signs of wear. Pages may include limited notes and highlighting. May NOT include discs, access code or other supplemental materials.
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Algorithmic Learning in a Random World
Seller: Books From California, Simi Valley, CA, U.S.A.
Seller rating 4 out of 5 stars
paperback. Condition: Fine.
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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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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: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 144.30
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Algorithmic Learning in a Random World
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
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Algorithmic Learning in a Random World
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Algorithmic Learning In A Random World
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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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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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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Condition: As New. Unread book in perfect condition.
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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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Condition: New.
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Condition: New.
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Algorithmic Learning In A Random World
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Algorithmic Learning in a Random World
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Algorithmic Learning in a Random World
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
£ 165.94
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Condition: New. 2nd ed. 2022 edition NO-PA16APR2015-KAP.
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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
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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.
