Algorithmic Learning Theory [[electronic resource] ] : 27th International Conference, ALT 2016, Bari, Italy, October 19-21, 2016, Proceedings / / edited by Ronald Ortner, Hans Ulrich Simon, Sandra Zilles

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016
• Edizione: 1st ed. 2016.
• Descrizione fisica: 1 online resource (XIX, 371 p. 21 illus.)
• Disciplina: 005.1
• Soggetto topico: Artificial intelligence; Computers; Data mining; Pattern recognition; Artificial Intelligence; Theory of Computation; Data Mining and Knowledge Discovery; Pattern Recognition
• Persona (resp. second.): OrtnerRonald; SimonHans Ulrich; ZillesSandra
• Nota di contenuto: Error bounds, sample compression schemes -- Statistical learning, theory, evolvability -- Exact and interactive learning -- Complexity of teaching models -- Inductive inference -- Online learning -- Bandits and reinforcement learning -- Clustering.
• Sommario/riassunto: This book constitutes the refereed proceedings of the 27th International Conference on Algorithmic Learning Theory, ALT 2016, held in Bari, Italy, in October 2016, co-located with the 19th International Conference on Discovery Science, DS 2016. The 24 regular papers presented in this volume were carefully reviewed and selected from 45 submissions. In addition the book contains 5 abstracts of invited talks. The papers are organized in topical sections named: error bounds, sample compression schemes; statistical learning, theory, evolvability; exact and interactive learning; complexity of teaching models; inductive inference; online learning; bandits and reinforcement learning; and clustering.
• Titolo autorizzato: Algorithmic Learning Theory
• ISBN: 3-319-46379-9
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996465274103316
• Lo trovi qui: Univ. di Salerno
• Serie: Lecture Notes in Artificial Intelligence ; ; 9925