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Computational Learning Theory [electronic resource] :15th Annual Conference on Computational Learning Theory, COLT 2002 Sydney, Australia, July 8–10, 2002 Proceedings /

Contributor(s): Kivinen, Jyrki [editor.] | Sloan, Robert H [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Computer Science: 2375Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2002.Description: XII, 412 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540454359.Subject(s): Computer science | Computers | Algorithms | Mathematical logic | Artificial intelligence | Computer Science | Artificial Intelligence (incl. Robotics) | Mathematical Logic and Formal Languages | Computation by Abstract Devices | Algorithm Analysis and Problem ComplexityOnline resources: Click here to access online
Contents:
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.
In: Springer eBooks
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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.

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