Algorithmic Learning in a Random World

Vladimir Vovk

ISBN 10: 3031066510 / ISBN 13: 9783031066511

Published by Springer International Publishing Dez 2023, 2023

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -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. 504 pp. Englisch. Seller Inventory # 9783031066511

Bibliographic Details

Title: Algorithmic Learning in a Random World

Publisher: Springer International Publishing Dez 2023

Publication Date: 2023

Binding: Taschenbuch

Condition: Neu

Edition: 2nd Edition