{"product_id":"fischer-paul-computational-learning-theory-9783540657019","title":"Computational Learning Theory","description":"Invited Lectures.- Theoretical Views of Boosting.- Open Theoretical Questions in Reinforcement Learning.- Learning from Random Examples.- A Geometric Approach to Leveraging Weak Learners.- Query by Committee, Linear Separation and Random Walks.- Hardness Results for Neural Network Approximation Problems.- Learning from Queries and Counterexamples.- Learnability of Quantified Formulas.- Learning Multiplicity Automata from Smallest Counterexamples.- Exact Learning when Irrelevant Variables Abound.- An Application of Codes to Attribute-Efficient Learning.- Learning Range Restricted Horn Expressions.- Reinforcement Learning.- On the Asymptotic Behavior of a Constant Stepsize Temporal-Difference Learning Algorithm.- On-line Learning and Expert Advice.- Direct and Indirect Algorithms for On-line Learning of Disjunctions.- Averaging Expert Predictions.- Teaching and Learning.- On Teaching and Learning Intersection-Closed Concept Classes.- Inductive Inference.- Avoiding Coding Tricks by Hyperrobust Learning.- Mind Change Complexity of Learning Logic Programs.- Statistical Theory of Learning and Pattern Recognition.- Regularized Principal Manifolds.- Distribution-Dependent Vapnik-Chervonenkis Bounds.- Lower Bounds on the Rate of Convergence of Nonparametric Pattern Recognition.- On Error Estimation for the Partitioning Classification Rule.- Margin Distribution Bounds on Generalization.- Generalization Performance of Classifiers in Terms of Observed Covering Numbers.- Entropy Numbers, Operators and Support Vector Kernels.","brand":"Springer","offers":[{"title":"Default Title","offer_id":54716170764630,"sku":null,"price":0.0,"currency_code":"EUR","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0925\/5829\/5382\/files\/product_image_9783540657019_1.jpg?v=1791310810","url":"https:\/\/www.momoxbooks.com\/products\/fischer-paul-computational-learning-theory-9783540657019","provider":"momox","version":"1.0","type":"link"}