学习理论
2006-12
湖北辞书出版社
Simon, Hans Ulrich; Lugosi, Gbor; Lugosi, G. Bor
656
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.
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
