LEADER 01021nam a22002411i 4500 001 991002335569707536 005 20030601190515.0 008 030925s1970 gr |||||||||||||||||gre 035 $ab12274525-39ule_inst 035 $aARCHE-032209$9ExL 040 $aBiblioteca Interfacoltà$bita$cA.t.i. Arché s.c.r.l. Pandora Sicilia s.r.l. 100 1 $aPapadiamantes, Alexandros$0184923 245 10$aHapanta ton neoellenon klassikon /$cPapadiamantes 260 $a[Athenai] :$bHetaireia hellenikon ekdoseon,$c[1970?] 300 $av. ;$c25 cm 700 1 $aPeranthes, Michales 907 $a.b12274525$b02-04-14$c08-10-03 912 $a991002335569707536 945 $aLE002 Gr. I L 1$cV. 1$g1$i2002000676382$lle002$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i12666427$z08-10-03 945 $aLE002 Gr. I L 1/a$cV. 2$g1$i2002000676481$lle002$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i12666439$z08-10-03 996 $aHapanta ton neoellenon klassikon$9153130 997 $aUNISALENTO 998 $ale002$b08-10-03$cm$da $e-$fgre$ggr $h0$i2 LEADER 04839nam 22006855 450 001 9910635392203321 005 20251113191012.0 010 $a3-031-06649-9 024 7 $a10.1007/978-3-031-06649-8 035 $a(MiAaPQ)EBC7157469 035 $a(Au-PeEL)EBL7157469 035 $a(CKB)25703771900041 035 $a(OCoLC)1355217709 035 $a(DE-He213)978-3-031-06649-8 035 $a(EXLCZ)9925703771900041 100 $a20221213d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAlgorithmic Learning in a Random World /$fby Vladimir Vovk, Alexander Gammerman, Glenn Shafer 205 $a2nd ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (490 pages) 225 1 $aMathematics and Statistics Series 311 08$aPrint version: Vovk, Vladimir Algorithmic Learning in a Random World Cham : Springer International Publishing AG,c2022 9783031066481 327 $a1. Introduction -- Part I Set prediction -- 2. Conformal prediction: general case and regression -- 3. Conformal prediction: classification and general case -- 4. Modifications of conformal predictors -- Part II Probabilistic prediction -- 5. Impossibility results -- 6. Probabilistic classification: Venn predictors -- 7. Probabilistic regression: conformal predictive systems -- Part III Testing randomness -- 8. Testing exchangeability -- 9. Efficiency of conformal testing -- 10. Non-conformal shortcut -- Part IV Online compression modelling -- 11. Generalized conformal prediction -- 12. Generalized Venn prediction and hypergraphical models -- 13. Contrasts and perspectives. 330 $aThis book is about conformal prediction, an approach to prediction that originated in machine learning in the late 1990s. The main feature of conformal prediction is the principled treatment of the reliability of predictions. The prediction algorithms described ? conformal predictors ? are provably valid in the sense that they evaluate the reliability of their own predictions in a way that is neither over-pessimistic nor over-optimistic (the latter being especially dangerous). The approach is still flexible enough to incorporate most of the existing powerful methods of machine learning. The book covers both key conformal predictors and the mathematical analysis of their properties. Algorithmic Learning in a Random World contains, in addition to proofs of validity, results about the efficiency of conformal predictors. The only assumption required for validity is that of "randomness" (the prediction algorithm is presented with independent and identically distributed examples); in later chapters, even the assumption of randomness is significantly relaxed. Interesting results about efficiency are established both under randomness and under stronger assumptions. Since publication of the First Edition in 2005 conformal prediction has found numerous applications in medicine and industry, and is becoming a popular machine-learning technique. This Second Edition contains three new chapters. One is about conformal predictive distributions, which are more informative than the set predictions produced by standard conformal predictors. Another is about the efficiency of ways of testing the assumption of randomness based on conformal prediction. The third new chapter harnesses conformal testing procedures for protecting machine-learning algorithms against changes in the distribution of the data. In addition, the existing chapters have been revised, updated, and expanded. 410 0$aMathematics and Statistics Series 606 $aMachine learning 606 $aComputer science$xMathematics 606 $aMathematical statistics 606 $aAlgorithms 606 $aArtificial intelligence 606 $aMachine Learning 606 $aProbability and Statistics in Computer Science 606 $aDesign and Analysis of Algorithms 606 $aArtificial Intelligence 615 0$aMachine learning. 615 0$aComputer science$xMathematics. 615 0$aMathematical statistics. 615 0$aAlgorithms. 615 0$aArtificial intelligence. 615 14$aMachine Learning. 615 24$aProbability and Statistics in Computer Science. 615 24$aDesign and Analysis of Algorithms. 615 24$aArtificial Intelligence. 676 $a518.1 676 $a519.287 700 $aVovk$b Vladimir$f1960-$0283970 702 $aGammerman$b A$g(Alexander), 702 $aShafer$b Glenn$f1946- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910635392203321 996 $aAlgorithmic learning in a random world$93088632 997 $aUNINA