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Algorithmic Learning Theory 19th International Conference, ALT 2008, Budapest, Hungary, October 13-16, 2008, Proceedings /
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 wit...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2008.
|
| Edición: | 1st ed. 2008. |
| Colección: | Lecture Notes in Artificial Intelligence,
5254 |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 245 | 1 | 0 | |a Algorithmic Learning Theory |h [electronic resource] : |b 19th International Conference, ALT 2008, Budapest, Hungary, October 13-16, 2008, Proceedings / |c edited by Yoav Freund, László Györfi, György Turán, Thomas Zeugmann. |
| 250 | |a 1st ed. 2008. | ||
| 264 | 1 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg : |b Imprint: Springer, |c 2008. | |
| 300 | |a XIII, 467 p. |b online resource. | ||
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| 490 | 1 | |a Lecture Notes in Artificial Intelligence, |x 2945-9141 ; |v 5254 | |
| 505 | 0 | |a 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. | |
| 520 | |a 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. | ||
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Natural language processing (Computer science). | |
| 650 | 0 | |a Digital humanities. | |
| 650 | 1 | 4 | |a Data Mining and Knowledge Discovery. |
| 650 | 2 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Natural Language Processing (NLP). |
| 650 | 2 | 4 | |a Digital Humanities. |
| 700 | 1 | |a Freund, Yoav. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Györfi, László. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Turán, György. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Zeugmann, Thomas. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783540880974 |
| 776 | 0 | 8 | |i Printed edition: |z 9783540879862 |
| 830 | 0 | |a Lecture Notes in Artificial Intelligence, |x 2945-9141 ; |v 5254 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-540-87987-9 |z Texto Completo |
| 912 | |a ZDB-2-SCS | ||
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| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||