Algorithmic Learning Theory : 24th International Conference, ALT 2013, Singapore, October 6-9, 2013, Proceedings / edited by Sanjay Jain, Rémi Munos, Frank Stephan, Thomas Zeugmann.

Format:

Book

Contributor:

Jaina, Sañjaya, editor.

Munos, Rémi, editor.

Stephan, Frank (Frank Christian), editor.

Zeugmann, Thomas, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 8139.

Lecture Notes in Artificial Intelligence ; 8139

Language:

English

Subjects (All):

Artificial intelligence.

Logic, Symbolic and mathematical.

Algorithms.

Computers.

Computer logic.

Pattern perception.

Artificial Intelligence.

Mathematical Logic and Formal Languages.

Algorithm Analysis and Problem Complexity.

Computation by Abstract Devices.

Logics and Meanings of Programs.

Pattern Recognition.

Local Subjects:

Artificial Intelligence.

Mathematical Logic and Formal Languages.

Algorithm Analysis and Problem Complexity.

Computation by Abstract Devices.

Logics and Meanings of Programs.

Pattern Recognition.

Physical Description:

1 online resource (XVIII, 397 pages) : 30 illustrations.

Edition:

First edition 2013.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.

System Details:

text file PDF

Summary:

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.

Contents:

Editors' 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 Nyström Method

Nonparametric Multiple Change Point Estimation in Highly Dependent Time Series.

Other Format:

Printed edition:

ISBN:

978-3-642-40935-6

9783642409356

Access Restriction:

Restricted for use by site license.