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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.

Contributor(s): Ortner, Ronald [editor.] | Simon, Hans Ulrich [editor.] | Zilles, Sandra [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Computer Science: 9925Publisher: Cham : Springer International Publishing : Imprint: Springer, 2016Description: XIX, 371 p. 21 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319463797.Subject(s): Computer science | Computers | Data mining | Artificial intelligence | Pattern recognition | Computer Science | Artificial Intelligence (incl. Robotics) | Theory of Computation | Data Mining and Knowledge Discovery | Pattern RecognitionAdditional physical formats: Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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.
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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.

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.

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