LEADER 00938cam2 22002531 450 001 SOBE00063992 005 20201113085223.0 100 $a20200629d1957 |||||ita|0103 ba 101 $afre 102 $aFR 200 0 $a2$e1807-1815$fCharles-Maurice Talleyrand-Perigord 210 $aParis$cPlon$d1957 215 $a530 p., [15] c. di tav.$d22 cm 461 1$1001E600200024456$12001 $aMémoires / Charles-Maurice Talleyrand-Perigord ; Introduction notes et etablissement du texte par Paul Louis Couchoud et Jean Paul Choucoud 700 1$aTalleyrand-Perigord$b, Charles-Maurice$3A600200040301$4070$0389797 801 0$aIT$bUNISOB$c20201113$gRICA 850 $aUNISOB 852 $aUNISOB$j900$m51863 912 $aSOBE00063992 940 $aM 102 Monografia moderna SBN 941 $aW 957 $a900$b001530$i-2$gSI$d51863$1rovito$2UNISOB$3UNISOB$420200629122325.0$520200629122359.0$6rovito 996 $a2$91761168 997 $aUNISOB LEADER 01456nam 2200313 i 4500 001 991004402927907536 005 20251022111406.0 008 251016s2020 -uka e b 001 0 eng d 020 $a9781108486828$qhardcopy 024 7 $a10.1017/9781108571401$2doi 040 $aBibl. Dip.le Aggr. Matematica e Fisica - Sez. Matematica$beng 082 04$a518.1 084 $aAMS 68Q-xx 084 $aLC QA402 100 1 $aLattimore, Tor$01854713 245 10$aBandit algorithms /$cTor Lattimore, Csaba Szepesvári 260 $aCambridge ;$aNew York, NY :$bCambridge University Press,$c2020 300 $axviii, 518 p. :$bill. ;$c26 cm 504 $aIncludes bibliographical references (p. [484]-511) and index 520 $aDecision-making in the face of uncertainty is a challenge in machine learning, and the multi-armed bandit model is a common framework to address it. This comprehensive introduction is an excellent reference for established researchers and a resource for graduate students interested in exploring stochastic, adversarial and Bayesian frameworks 650 24$aResource allocation$xMathematical models 650 24$aDecision making$xMathematical models 650 14$aAlgorithms 650 14$aProbabilities 650 14$aMathematical optimization 700 1 $aSzepesvári, Csaba$eauthor$4http://id.loc.gov/vocabulary/relators/aut$01854714 912 $a991004402927907536 996 $aBandit algorithms$94452332 997 $aUNISALENTO