Algorithmic Learning Theory

Inhaltsverzeichnis

• Editors’ Introduction.
• Invited Papers.
• Invention and Artificial Intelligence.
• The Arrowsmith Project: 2005 Status Report.
• The Robot Scientist Project.
• Algorithms and Software for Collaborative Discovery from Autonomous, Semantically Heterogeneous, Distributed Information Sources.
• Training Support Vector Machines via SMO-Type Decomposition Methods.
• Kernel-Based Learning.
• Measuring Statistical Dependence with Hilbert-Schmidt Norms.
• An Analysis of the Anti-learning Phenomenon for the Class Symmetric Polyhedron.
• Learning Causal Structures Based on Markov Equivalence Class.
• Stochastic Complexity for Mixture of Exponential Families in Variational Bayes.
• ACME: An Associative Classifier Based on Maximum Entropy Principle.
• Constructing Multiclass Learners from Binary Learners: A Simple Black-Box Analysis of the Generalization Errors.
• On Computability of Pattern Recognition Problems.
• PAC-Learnability of Probabilistic Deterministic Finite State Automata in Terms of Variation Distance.
• Learnability of Probabilistic Automata via Oracles.
• Learning Attribute-Efficiently with Corrupt Oracles.
• Learning DNF by Statistical and Proper Distance Queries Under the Uniform Distribution.
• Learning of Elementary Formal Systems with Two Clauses Using Queries.
• Gold-Style and Query Learning Under Various Constraints on the Target Class.
• Non U-Shaped Vacillatory and Team Learning.
• Learning Multiple Languages in Groups.
• Inferring Unions of the Pattern Languages by the Most Fitting Covers.
• Identification in the Limit of Substitutable Context-Free Languages.
• Algorithms for Learning Regular Expressions.
• A Class of Prolog Programs with Non-linear Outputs Inferable from Positive Data.
• Absolute Versus Probabilistic Classification in a Logical Setting.
• Online Allocation with Risk Information.
• Defensive Universal Learning with Experts.
• On Following the Perturbed Leader in the Bandit Setting.
• Mixture of Vector Experts.
• On-line Learning with Delayed Label Feedback.
• Monotone Conditional Complexity Bounds on Future Prediction Errors.
• Non-asymptotic Calibration and Resolution.
• Defensive Prediction with Expert Advice.
• Defensive Forecasting for Linear Protocols.
• Teaching Learners with Restricted Mind Changes.