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Algorithmic Learning Theory 17th International Conference, ALT 2006, Barcelona, Spain, October 7-10, 2006, Proceedings /
| Cote: | Libro Electrónico |
|---|---|
| Collectivité auteur: | |
| Autres auteurs: | , , |
| Format: | Électronique eBook |
| Langue: | Inglés |
| Publié: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2006.
|
| Édition: | 1st ed. 2006. |
| Collection: | Lecture Notes in Artificial Intelligence,
4264 |
| Sujets: | |
| Accès en ligne: | Texto Completo |
Table des matières:
- Editors' Introduction
- Editors' Introduction
- Invited Contributions
- Solving Semi-infinite Linear Programs Using Boosting-Like Methods
- e-Science and the Semantic Web: A Symbiotic Relationship
- Spectral Norm in Learning Theory: Some Selected Topics
- Data-Driven Discovery Using Probabilistic Hidden Variable Models
- Reinforcement Learning and Apprenticeship Learning for Robotic Control
- Regular Contributions
- Learning Unions of ?(1)-Dimensional Rectangles
- On Exact Learning Halfspaces with Random Consistent Hypothesis Oracle
- Active Learning in the Non-realizable Case
- How Many Query Superpositions Are Needed to Learn?
- Teaching Memoryless Randomized Learners Without Feedback
- The Complexity of Learning SUBSEQ (A)
- Mind Change Complexity of Inferring Unbounded Unions of Pattern Languages from Positive Data
- Learning and Extending Sublanguages
- Iterative Learning from Positive Data and Negative Counterexamples
- Towards a Better Understanding of Incremental Learning
- On Exact Learning from Random Walk
- Risk-Sensitive Online Learning
- Leading Strategies in Competitive On-Line Prediction
- Hannan Consistency in On-Line Learning in Case of Unbounded Losses Under Partial Monitoring
- General Discounting Versus Average Reward
- The Missing Consistency Theorem for Bayesian Learning: Stochastic Model Selection
- Is There an Elegant Universal Theory of Prediction?
- Learning Linearly Separable Languages
- Smooth Boosting Using an Information-Based Criterion
- Large-Margin Thresholded Ensembles for Ordinal Regression: Theory and Practice
- Asymptotic Learnability of Reinforcement Problems with Arbitrary Dependence
- Probabilistic Generalization of Simple Grammars and Its Application to Reinforcement Learning
- Unsupervised Slow Subspace-Learning from Stationary Processes
- Learning-Related Complexity of Linear Ranking Functions.