Algorithmic Learning Theory  27th International Conference, ALT 2016, Bari, Italy, October 19-21, 2016, Proceedings /  [electronic resource] : 
edited by Ronald Ortner, Hans Ulrich Simon, Sandra Zilles. 
 - 1st ed. 2016. 
 - XIX, 371 p. 21 illus.  online resource. 
 - Lecture Notes in Artificial Intelligence,  9925  2945-9141 ; .
 - Lecture Notes in Artificial Intelligence,  9925 .
<br/><br/>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. 
<br/><br/> 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. 
<br/><br/><label>ISBN: </label>9783319463797 
<br/><br/><label>Standard No.: </label>10.1007/978-3-319-46379-7  doi 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Artificial intelligence.<br/>Computer science.<br/>Data mining.<br/>Pattern recognition systems.<br/>Artificial Intelligence.<br/>Theory of Computation.<br/>Data Mining and Knowledge Discovery.<br/>Automated Pattern Recognition.
<br/><br/><label>LC Class. No.: </label>Q334-342 TA347.A78 
<br/><br/><label>Dewey Class. No.: </label>006.3