学习理论

出版时间：2006-12  出版社：湖北辞书出版社  作者：Simon, Hans Ulrich; Lugosi, Gbor; Lugosi, G. Bor  页数：656  

内容概要

This book constitutes the refereed proceedings of the 19th Annual Conference on Learning Theory, COLT 2006, held in Pittsburgh, Pennsylvania, USA in June 2006. The 43 revised full papers presented together with 2 articles on open problems and 3 invited lectures were carefully reviewed and selected from a total of 102 submissions. The papers cover a wide range of topics including clustering, un- and semisupervised learning, statistical learning theory, regularized learning and kernel methods, query learning and teaching, inductive inference, learning algorithms and limitations on learning, online aggregation, online prediction and reinforcement learning.

书籍目录

Invited Presentations  Random Multivariate Search Trees  On Learning and Logic  Predictions as Statements and DecisionsClustering, Un-, and Semisupervised Learning  A Sober Look at Clustering Stability  PAC Learning Axis-Aligned Mixtures of Gaussians with No Separation Assumption  Stable Transductive Learning  Uniform Convergence of Adaptive Graph-Based RegularizationStatistical Learning Theory  The Rademacher Complexity of Linear Transformation Classes  Function Classes That Approximate the Bayes Risk  Functional Classification with Margin Conditions  Significance and Recovery of Block Structures in Binary Matrices with NoiseRegularized Learning and Kernel Methods  Maximum Entropy Distribution Estimation with Generalized Regularization  Unifying Divergence Minimization and Statistical Inference Via Convex Duality  Mercer's Theorem, Feature Maps, and Smoothing  Learning Bounds for Support Vector Machines with Learned KernelsQuery Learning and Teaching  On Optimal Learning Algorithms for Multiplicity Automata  Exact Learning Composed Classes with a Small Number of Mistakes  DNF Are Teachable in the Average Case  Teaching Randomized LearnersInductive Inference  Memory-Limited U-Shaped Learning  On Learning Languages from Positive Data and a Limited Number of Short Counterexamples  Learning Rational Stochastic Languages  Parent Assignment Is Hard for the MDL, AIC, and NML CostsLearning Algorithms and Limitations on LearningOnline AggregationOnline Prediction and Reinforcement Learning ⅠOnline Prediction and Reinforcement Learning ⅡOnline Prediction and Reinforcement Learning ⅢOther ApproachesOpen ProblemsAuthor Index