Computational learning theory : 15th Annual Conference on Computational Learning Theory, COLT 2002, Sydney, Australia, July 8 10, 2002 : proceedings
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| Institution som forfatter: | |
|---|---|
| Andre forfattere: | , |
| Format: | Livre numérique |
| Sprog: | Anglais |
| Udgivet: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Serier: | Lecture notes in computer science. Lecture notes in artificial intelligence
2375 |
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| Online adgang: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Kommentar: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Computational learning theory, 15th Annual Conference on Computational Learning Theory, COLT 2002, Sydney, Australia, July 8-10, 2002, Proceedings, Jyrki Kivinen, Robert H. Sloan, eds, Berlin, Springer, 2002, 1 vol. (XI-395 p.), Lecture notes in computer science, 3-540-43836-X • Computational Learning Theory, Texte imprimé, 9783662167175 |
Indholdsfortegnelse:
- Statistical Learning Theory
- Agnostic Learning Nonconvex Function Classes
- Entropy, Combinatorial Dimensions and Random Averages
- Geometric Parameters of Kernel Machines
- Localized Rademacher Complexities
- Some Local Measures of Complexity of Convex Hulls and Generalization Bounds
- Online Learning
- Path Kernels and Multiplicative Updates
- Predictive Complexity and Information
- Mixability and the Existence of Weak Complexities
- A Second-Order Perceptron Algorithm
- Tracking Linear-Threshold Concepts with Winnow
- Inductive Inference
- Learning Tree Languages from Text
- Polynomial Time Inductive Inference of Ordered Tree Patterns with Internal Structured Variables from Positive Data
- Inferring Deterministic Linear Languages
- Merging Uniform Inductive Learners
- The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions
- PAC Learning
- New Lower Bounds for Statistical Query Learning
- Exploring Learnability between Exact and PAC
- PAC Bounds for Multi-armed Bandit and Markov Decision Processes
- Bounds for the Minimum Disagreement Problem with Applications to Learning Theory
- On the Proper Learning of Axis Parallel Concepts
- Boosting
- A Consistent Strategy for Boosting Algorithms
- The Consistency of Greedy Algorithms for Classification
- Maximizing the Margin with Boosting
- Other Learning Paradigms
- Performance Guarantees for Hierarchical Clustering
- Self-Optimizing and Pareto-Optimal Policies in General Environments Based on Bayes-Mixtures
- Prediction and Dimension
- Invited Talk
- Learning the Internet.