Algorithmic Learning Theory: 19th International Conference, ALT 2008, Budapest, Hungary, October 13-16, 2008, Proceedings / Edition 1

Algorithmic Learning Theory: 19th International Conference, ALT 2008, Budapest, Hungary, October 13-16, 2008, Proceedings / Edition 1

by Yoav Freund
     
 

ISBN-10: 3540879862

ISBN-13: 9783540879862

Pub. Date: 09/28/2008

Publisher: Springer Berlin Heidelberg

This book constitutes the refereed proceedings of the 19th International Conference on Algorithmic Learning Theory, ALT 2008, held in Budapest, Hungary, in October 2008, co-located with the 11th International Conference on Discovery Science, DS 2008.

The 31 revised full papers presented together with the abstracts of 5 invited talks were carefully reviewed and

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Overview

This book constitutes the refereed proceedings of the 19th International Conference on Algorithmic Learning Theory, ALT 2008, held in Budapest, Hungary, in October 2008, co-located with the 11th International Conference on Discovery Science, DS 2008.

The 31 revised full papers presented together with the abstracts of 5 invited talks were carefully reviewed and selected from 46 submissions. The papers are dedicated to the theoretical foundations of machine learning; they address topics such as statistical learning; probability and stochastic processes; boosting and experts; active and query learning; and inductive inference.

Product Details

ISBN-13:
9783540879862
Publisher:
Springer Berlin Heidelberg
Publication date:
09/28/2008
Series:
Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence Series, #5254
Edition description:
2008
Pages:
467
Product dimensions:
6.10(w) x 9.30(h) x 1.10(d)

Table of Contents

Invited Papers.- On Iterative Algorithms with an Information Geometry Background.- Visual Analytics: Combining Automated Discovery with Interactive Visualizations.- Some Mathematics behind Graph Property Testing.- Finding Total and Partial Orders from Data for Seriation.- Computational Models of Neural Representations in the Human Brain.- Regular Contributions.- Generalization Bounds for Some Ordinal Regression Algorithms.- Approximation of the Optimal ROC Curve and a Tree-Based Ranking Algorithm.- Sample Selection Bias Correction Theory.- Exploiting Cluster-Structure to Predict the Labeling of a Graph.- A Uniform Lower Error Bound for Half-Space Learning.- Generalization Bounds for K-Dimensional Coding Schemes in Hilbert Spaces.- Learning and Generalization with the Information Bottleneck.- Growth Optimal Investment with Transaction Costs.- Online Regret Bounds for Markov Decision Processes with Deterministic Transitions.- On-Line Probability, Complexity and Randomness.- Prequential Randomness.- Some Sufficient Conditions on an Arbitrary Class of Stochastic Processes for the Existence of a Predictor.- Nonparametric Independence Tests: Space Partitioning and Kernel Approaches.- Supermartingales in Prediction with Expert Advice.- Aggregating Algorithm for a Space of Analytic Functions.- Smooth Boosting for Margin-Based Ranking.- Learning with Continuous Experts Using Drifting Games.- Entropy Regularized LPBoost.- Optimally Learning Social Networks with Activations and Suppressions.- Active Learning in Multi-armed Bandits.- Query Learning and Certificates in Lattices.- Clustering with Interactive Feedback.- Active Learning of Group-Structured Environments.- Finding the Rare Cube.- Iterative Learning of Simple External Contextual Languages.- Topological Properties of Concept Spaces.- Dynamically Delayed Postdictive Completeness and Consistency in Learning.- Dynamic Modeling in Inductive Inference.- Optimal Language Learning.- Numberings Optimal for Learning.- Learning with Temporary Memory.- Erratum: Constructing Multiclass Learners from Binary Learners: A Simple Black-Box Analysis of the Generalization Errors.

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