Algorithmic Learning Theory 24th International Conference, ALT 2013, Singapore, October 6-9, 2013. Proceedings /

This book constitutes the proceedings of the 24th International Conference on Algorithmic Learning Theory, ALT 2013, held in Singapore in October 2013, and co-located with the 16th International Conference on Discovery Science, DS 2013. The 23 papers presented in this volume were carefully reviewed...

Full description

Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Jain, Sanjay. (Editor), Munos, Rm̌i. (Editor), Stephan, Frank. (Editor), Zeugmann, Thomas. (Editor)
Format: Electronic
Language:English
Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
Series:Lecture Notes in Computer Science, 8139
Subjects:
Online Access:https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-3-642-40935-6
LEADER 04698nam a22006015i 4500
001 16365
003 DE-He213
005 20131001034457.0
007 cr nn 008mamaa
008 130927s2013 gw | s |||| 0|eng d
020 # # |a 9783642409356  |9 978-3-642-40935-6 
024 7 # |a 10.1007/978-3-642-40935-6  |2 doi 
050 # 4 |a Q334-342 
050 # 4 |a TJ210.2-211.495 
072 # 7 |a UYQ  |2 bicssc 
072 # 7 |a TJFM1  |2 bicssc 
072 # 7 |a COM004000  |2 bisacsh 
082 0 4 |a 006.3  |2 23 
100 1 # |a Jain, Sanjay.  |e editor. 
245 1 0 |a Algorithmic Learning Theory  |b 24th International Conference, ALT 2013, Singapore, October 6-9, 2013. Proceedings /  |c edited by Sanjay Jain, Rm̌i Munos, Frank Stephan, Thomas Zeugmann.  |h [electronic resource] : 
264 # 1 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg :  |b Imprint: Springer,  |c 2013. 
300 # # |a XVIII, 397 p. 30 illus.  |b online resource. 
336 # # |a text  |b txt  |2 rdacontent 
337 # # |a computer  |b c  |2 rdamedia 
338 # # |a online resource  |b cr  |2 rdacarrier 
347 # # |a text file  |b PDF  |2 rda 
490 1 # |a Lecture Notes in Computer Science,  |v 8139  |x 0302-9743 ; 
505 0 # |a Editors<U+0019> Introduction -- Learning and Optimizing with Preferences -- Efficient Algorithms for Combinatorial Online Prediction -- Exact Learning from Membership Queries: Some Techniques, Results and New Directions -- Online Learning Universal Algorithm for Trading in Stock Market Based on the Method of Calibration -- Combinatorial Online Prediction via Metarounding -- On Competitive Recommendations -- Online PCA with Optimal Regrets -- Inductive Inference and Grammatical Inference Partial Learning of Recursively Enumerable Languages -- Topological Separations in Inductive Inference -- PAC Learning of Some Subclasses of Context-Free Grammars with Basic Distributional Properties from Positive Data -- Universal Knowledge-Seeking Agents for Stochastic Environments -- Teaching and Learning from Queries Order Compression Schemes -- Learning a Bounded-Degree Tree Using Separator Queries -- Faster Hoeffding Racing: Bernstein Races via Jackknife Estimates -- Robust Risk-Averse Stochastic Multi-armed Bandits -- An Efficient Algorithm for Learning with Semi-bandit Feedback -- Differentially-Private Learning of Low Dimensional Manifolds -- Generalization and Robustness of Batched Weighted Average Algorithm with V-Geometrically Ergodic Markov Data -- Adaptive Metric Dimensionality Reduction -- Dimension-Adaptive Bounds on Compressive FLD Classification -- Bayesian Methods for Low-Rank Matrix Estimation: Short Survey and Theoretical Study -- Concentration and Confidence for Discrete Bayesian Sequence Predictors -- Algorithmic Connections between Active Learning and Stochastic Convex Optimization -- Unsupervised/Semi-Supervised Learning Unsupervised Model-Free Representation Learning -- Fast Spectral Clustering via the Nystrm̲ Method -- Nonparametric Multiple Change Point Estimation in Highly Dependent Time Series. 
520 # # |a This book constitutes the proceedings of the 24th International Conference on Algorithmic Learning Theory, ALT 2013, held in Singapore in October 2013, and co-located with the 16th International Conference on Discovery Science, DS 2013. The 23 papers presented in this volume were carefully reviewed and selected from 39 submissions. In addition the book contains 3 full papers of invited talks. The papers are organized in topical sections named: online learning, inductive inference and grammatical inference, teaching and learning from queries, bandit theory, statistical learning theory, Bayesian/stochastic learning, and unsupervised/semi-supervised learning. 
650 # 0 |a Computer science. 
650 # 0 |a Computer software. 
650 # 0 |a Logic design. 
650 # 0 |a Artificial intelligence. 
650 # 0 |a Optical pattern recognition. 
650 1 4 |a Computer Science. 
650 2 4 |a Artificial Intelligence (incl. Robotics). 
650 2 4 |a Mathematical Logic and Formal Languages. 
650 2 4 |a Algorithm Analysis and Problem Complexity. 
650 2 4 |a Computation by Abstract Devices. 
650 2 4 |a Logics and Meanings of Programs. 
650 2 4 |a Pattern Recognition. 
700 1 # |a Munos, Rm̌i.  |e editor. 
700 1 # |a Stephan, Frank.  |e editor. 
700 1 # |a Zeugmann, Thomas.  |e editor. 
710 2 # |a SpringerLink (Online service) 
773 0 # |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9783642409349 
830 # 0 |a Lecture Notes in Computer Science,  |v 8139  |x 0302-9743 ; 
856 4 0 |u https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-3-642-40935-6 
912 # # |a ZDB-2-SCS 
912 # # |a ZDB-2-LNC 
950 # # |a Computer Science (Springer-11645)