Algorithmic Learning Theory [electronic resource] : 19th International Conference, ALT 2008, Budapest, Hungary, October 13-16, 2008, Proceedings / edited by Yoav Freund, László Györfi, György Turán, Thomas Zeugmann.
Contributor(s): Freund, Yoav [editor.] | Györfi, László [editor.] | Turán, György [editor.] | Zeugmann, Thomas [editor.] | SpringerLink (Online service).
Material type: BookSeries: Lecture Notes in Artificial Intelligence: 5254Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2008Edition: 1st ed. 2008.Description: XIII, 467 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540879879.Subject(s): Data mining | Artificial intelligence | Natural language processing (Computer science) | Digital humanities | Data Mining and Knowledge Discovery | Artificial Intelligence | Natural Language Processing (NLP) | Digital HumanitiesAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.312 Online resources: Click here to access online
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Springer Nature eBookSummary: 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.
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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