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[null,"1.","Record Nr.",null,"UNISALENTO991004402927907536"]
[null,null,"Autore",null,"Lattimore, Tor"]
[null,null,"Titolo",null,"Bandit algorithms / Tor Lattimore, Csaba Szepesvári"]
[null,null,"Pubbl/distr/stampa",null,"Cambridge ; New York, NY : Cambridge University Press, 2020"]
[null,null,"ISBN",null,"9781108486828"]
[null,null,"Descrizione fisica",null,"xviii, 518 p. : ill. ; 26 cm"]
[null,null,"Classificazione",null,"AMS 68Q-xx"]
[{"$nulls":4},"LC QA402"]
[null,null,"Disciplina",null,"518.1"]
[null,null,"Soggetti",null,"Resource allocation - Mathematical models"]
[{"$nulls":4},"Decision making - Mathematical models"]
[{"$nulls":4},"Algorithms"]
[{"$nulls":4},"Probabilities"]
[{"$nulls":4},"Mathematical optimization"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references (p. [484]-511) and index"]
[null,null,null,"Sommario/riassunto","Decision-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"]