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001451889 001__ 1451889
001451889 003__ OCoLC
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001451889 019__ $$a1354992744$$a1367358741
001451889 020__ $$a9783031066498$$q(electronic bk.)
001451889 020__ $$a3031066499$$q(electronic bk.)
001451889 020__ $$z9783031066481
001451889 020__ $$z3031066480
001451889 0247_ $$a10.1007/978-3-031-06649-8$$2doi
001451889 035__ $$aSP(OCoLC)1355217709
001451889 040__ $$aEBLCP$$beng$$cEBLCP$$dGW5XE$$dYDX$$dFIE$$dUKAHL
001451889 049__ $$aISEA
001451889 050_4 $$aQA279.2
001451889 08204 $$a519.2/87$$223/eng/20230106
001451889 1001_ $$aVovk, Vladimir,$$d1960-
001451889 24510 $$aAlgorithmic learning in a random world /$$cVladimir Vovk, Alexander Gammerman, Glenn Shafer.
001451889 250__ $$a2nd ed.
001451889 260__ $$aCham :$$bSpringer,$$c2022.
001451889 300__ $$a1 online resource (490 p.)
001451889 336__ $$atext$$btxt$$2rdacontent
001451889 337__ $$acomputer$$bc$$2rdamedia
001451889 338__ $$aonline resource$$bcr$$2rdacarrier
001451889 500__ $$a2.9.5 Examples of Nonconformity Measures
001451889 5050_ $$aIntro -- Contents -- Preface to the Second Edition -- Preface to the First Edition -- Notation and Abbreviations -- Sets, Bags, and Sequences -- Stochastics -- Machine Learning -- Programming -- Confidence Prediction -- Other Notations -- Abbreviations -- 1 Introduction -- 1.1 Machine Learning -- 1.1.1 Learning Under Randomness -- 1.1.2 Learning Under Unconstrained Randomness -- 1.2 A Shortcoming of Statistical Learning Theory -- 1.2.1 The Hold-Out Estimate of Confidence -- 1.2.2 The Contribution of This Book -- 1.3 The Online Framework -- 1.3.1 Online Learning
001451889 5058_ $$a1.3.2 Online/Offline Compromises -- 1.3.3 One-Off and Offline Learning -- 1.3.4 Induction, Transduction, and the Online Framework -- 1.4 Conformal Prediction -- 1.4.1 Nested Prediction Sets -- 1.4.2 Validity -- 1.4.3 Efficiency -- 1.4.4 Conditionality -- 1.4.5 Flexibility of Conformal Predictors -- 1.5 Probabilistic Prediction Under Unconstrained Randomness -- 1.5.1 Universally Consistent Probabilistic Predictor -- 1.5.2 Probabilistic Prediction Using a Finite Dataset -- 1.5.3 Venn Prediction -- 1.5.4 Conformal Predictive Distributions -- 1.6 Beyond Randomness -- 1.6.1 Testing Randomness
001451889 5058_ $$a1.6.2 Online Compression Models -- 1.7 Context -- Part I Set Prediction -- 2 Conformal Prediction: General Case and Regression -- 2.1 Confidence Predictors -- 2.1.1 Assumptions -- 2.1.2 Simple Predictors and Confidence Predictors -- 2.1.3 Validity -- 2.1.4 Randomized Confidence Predictors -- 2.1.5 Confidence Predictors Over a Finite Horizon -- 2.1.6 One-Off and Offline Confidence Predictors -- 2.2 Conformal Predictors -- 2.2.1 Bags -- 2.2.2 Nonconformity and Conformity -- 2.2.3 p-Values -- 2.2.4 Definition of Conformal Predictors -- 2.2.5 Validity -- 2.2.6 Smoothed Conformal Predictors
001451889 5058_ $$a2.2.7 Finite-Horizon Conformal Prediction -- 2.2.8 One-Off and Offline Conformal Predictors -- 2.2.9 General Schemes for Defining Nonconformity -- Conformity to a Bag -- Conformity to a Property -- 2.2.10 Deleted Conformity Measures -- 2.3 Conformalized Ridge Regression -- 2.3.1 Least Squares and Ridge Regression -- 2.3.2 Basic CRR -- 2.3.3 Two Modifications -- 2.3.4 Dual Form Ridge Regression -- 2.4 Conformalized Nearest Neighbours Regression -- 2.5 Efficiency of Conformalized Ridge Regression -- 2.5.1 Hard and Soft Models -- 2.5.2 Bayesian Ridge Regression -- 2.5.3 Efficiency of CRR
001451889 5058_ $$a2.6 Are There Other Ways to Achieve Validity? -- 2.7 Conformal Transducers -- 2.7.1 Definitions and Properties of Validity -- 2.7.2 Normalized Confidence Predictors and Confidence Transducers -- 2.8 Proofs -- 2.8.1 Proof of Theorem 2.2 -- 2.8.2 Proof of Theorem 2.7 -- Regularizing the Rays in Upper CRR -- Proof Proper -- 2.8.3 Proof of Theorem 2.10 -- 2.9 Context -- 2.9.1 Exchangeability vs Randomness -- 2.9.2 Conformal Prediction -- 2.9.3 Two Equivalent Definitions of Nonconformity Measures -- 2.9.4 The Two Meanings of Conformity in Conformal Prediction
001451889 506__ $$aAccess limited to authorized users.
001451889 520__ $$aThis 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.
001451889 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed January 6, 2023).
001451889 650_0 $$aPrediction theory.
001451889 650_0 $$aAlgorithms.
001451889 650_0 $$aStochastic processes.
001451889 655_0 $$aElectronic books.
001451889 7001_ $$aGammerman, A.$$q(Alexander)
001451889 7001_ $$aShafer, Glenn,$$d1946-
001451889 77608 $$iPrint version:$$aVovk, Vladimir$$tAlgorithmic Learning in a Random World$$dCham : Springer International Publishing AG,c2022$$z9783031066481
001451889 852__ $$bebk
001451889 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-06649-8$$zOnline Access$$91397441.1
001451889 909CO $$ooai:library.usi.edu:1451889$$pGLOBAL_SET
001451889 980__ $$aBIB
001451889 980__ $$aEBOOK
001451889 982__ $$aEbook
001451889 983__ $$aOnline
001451889 994__ $$a92$$bISE