Computational Learning Theory 15th Annual Conference on Computational Learning Theory, COLT 2002, Sydney, Australia, July 8-10, 2002. Proceedings /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Kivinen, Jyrki (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Sloan, Robert H. (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2002.
• Έκδοση: 1st ed. 2002.
• Σειρά: Lecture Notes in Artificial Intelligence ; 2375
• Θέματα: Artificial intelligence.; Mathematical logic.; Computers.; Algorithms.; Artificial Intelligence.; Mathematical Logic and Formal Languages.; Computation by Abstract Devices.; Algorithm Analysis and Problem Complexity.

Πίνακας περιεχομένων:

• 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.