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.

 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.