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E-grāmata: Algorithmic Learning Theory: 19th International Conference, ALT 2008, Budapest, Hungary, October 13-16, 2008, Proceedings

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  • Formāts: PDF+DRM
  • Sērija : Lecture Notes in Computer Science 5254
  • Izdošanas datums: 02-Oct-2008
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Valoda: eng
  • ISBN-13: 9783540879879
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Algorithmic Learning Theory: 19th International Conference, ALT 2008, Budapest, Hungary, October 13-16, 2008, ProceedingsPalielināt
  • Formāts: PDF+DRM
  • Sērija : Lecture Notes in Computer Science 5254
  • Izdošanas datums: 02-Oct-2008
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Valoda: eng
  • ISBN-13: 9783540879879
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Constitutes the 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. This book includes the papers that are dedicated to the theoretical foundations of machine learning.

This volume contains papers presented at the 19th International Conference on Algorithmic Learning Theory (ALT 2008), which was held in Budapest, Hungary during October 13–16, 2008. The conference was co-located with the 11th - ternational Conference on Discovery Science (DS 2008). The technical program of ALT 2008 contained 31 papers selected from 46 submissions, and 5 invited talks. The invited talks were presented in joint sessions of both conferences. ALT 2008 was the 19th in the ALT conference series, established in Japan in 1990. The series Analogical and Inductive Inference is a predecessor of this series: it was held in 1986, 1989 and 1992, co-located with ALT in 1994, and s- sequently merged with ALT. ALT maintains its strong connections to Japan, but has also been held in other countries, such as Australia, Germany, Italy, Sin- pore, Spain and the USA. The ALT conference series is supervised by its Steering Committee: Naoki Abe (IBM T. J.
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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