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Ajouter au panierPaperback. Etat : New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 7-12 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
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Ajouter au panierhardcover. Etat : New. In shrink wrap. Looks like an interesting title!
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Ajouter au panierPaperback. Etat : Brand New. 344 pages. 9.25x6.10x0.77 inches. In Stock.
Langue: anglais
Edité par Springer International Publishing AG, Cham, 2022
ISBN 10 : 3031066480 ISBN 13 : 9783031066481
Vendeur : Grand Eagle Retail, Bensenville, IL, Etats-Unis
EUR 180,38
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Ajouter au panierHardcover. Etat : 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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Ajouter au panierEtat : New.
Langue: anglais
Edité par Springer International Publishing AG, Cham, 2023
ISBN 10 : 3031066510 ISBN 13 : 9783031066511
Vendeur : Grand Eagle Retail, Bensenville, IL, Etats-Unis
EUR 190,65
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Ajouter au panierPaperback. Etat : 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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Ajouter au panierEtat : New.
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Ajouter au panierHardcover. Etat : Gut. 340 pp. Cover discolored at the spine, otherwise well preserved copy 350 Sprache: Englisch Gewicht in Gramm: 808.
EUR 196,08
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Ajouter au panierEtat : As New. Unread book in perfect condition.
EUR 197,26
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Ajouter au panierEtat : As New. Unread book in perfect condition.
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Ajouter au panierEtat : As New. Unread book in perfect condition.
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Ajouter au panierEtat : New.
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Ajouter au panierEtat : New.
Langue: anglais
Edité par Springer-Verlag New York Inc., New York, NY, 2005
ISBN 10 : 0387001522 ISBN 13 : 9780387001524
Vendeur : Grand Eagle Retail, Bensenville, IL, Etats-Unis
EUR 208,08
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Ajouter au panierHardcover. Etat : 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.
EUR 197,35
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Ajouter au panierEtat : As New. Unread book in perfect condition.
EUR 197,27
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Ajouter au panierEtat : As New. Unread book in perfect condition.