Algorithmic Learning Theory (eBook, PDF)

19th International Conference, ALT 2008, Budapest, Hungary, October 13-16, 2008, Proceedings
Redaktion: Freund, Yoav; Zeugmann, Thomas; Turán, György; Györfi, László

• Format: PDF

Produktbeschreibung

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.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

---

Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Verlag: Springer Berlin Heidelberg
• Seitenzahl: 467
• Erscheinungstermin: 2. Oktober 2008
• Englisch
• ISBN-13: 9783540879879
• Artikelnr.: 44133701

Autorenporträt

Yoav Freund is Professor of Computer Science at the University of California, San Diego.

Inhaltsangabe

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.