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Algorithmic Learning Random World by Vovk Vladimir (49 results)
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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
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Algorithmic Learning In A Random World
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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: Ria Christie Collections, Uxbridge, United Kingdom
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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
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Algorithmic Learning in a Random World (Paperback)
Published by Springer International Publishing AG, Cham, 2023
ISBN 10: 3031066510 ISBN 13: 9783031066511
Language: English
Paperback. Condition: new. Paperback. 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 (Hardcover)
Published by Springer International Publishing AG, Cham, 2022
ISBN 10: 3031066480 ISBN 13: 9783031066481
Language: English
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.
-
Algorithmic Learning in a Random World (Hardcover)
Published by Springer-Verlag New York Inc., New York, NY, 2005
ISBN 10: 0387001522 ISBN 13: 9780387001524
Language: English
Hardcover. Condition: new. Hardcover. Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. Based on these approximations, a new set of machine learning algorithms have been developed that can be used to make predictions and to estimate their confidence and credibility in high-dimensional spaces under the usual assumption that the data are independent and identically distributed (assumption of randomness). Another aim of this unique monograph is to outline some limits of predictions: The approach based on algorithmic theory of randomness allows for the proof of impossibility of prediction in certain situations. The book describes how several important machine learning problems, such as density estimation in high-dimensional spaces, cannot be solved if the only assumption is randomness. A scientific monograph developing significant new algorithmic foundations in machine learning theory. 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
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US$ 228.75
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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
US$ 228.82
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Condition: Very Good. Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc.
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Condition: New. 2nd ed. 2022 edition NO-PA16APR2015-KAP.
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Algorithmic Learning in a Random World
Published by Springer International Publishing, 2022
ISBN 10: 3031066480 ISBN 13: 9783031066481
Language: English
US$ 219.26
Convert currencyUS$ 75.58 shipping from Germany to U.S.A.Quantity: 1 available
Add to basketBuch. 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
Published by Springer International Publishing, 2023
ISBN 10: 3031066510 ISBN 13: 9783031066511
Language: English
US$ 219.26
Convert currencyUS$ 74.64 shipping from Germany to U.S.A.Quantity: 1 available
Add to basketTaschenbuch. 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.
