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Computational Learning Theory [electronic resource] :4th European Conference, EuroCOLT’99 Nordkirchen, Germany, March 29–31, 1999 Proceedings /

Contributor(s): Fischer, Paul [editor.] | Simon, Hans Ulrich [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Computer Science: 1572Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 1999.Description: X, 299 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540490975.Subject(s): Computer science | Computers | Algorithms | Mathematical logic | Artificial intelligence | Pattern recognition | Computer Science | Artificial Intelligence (incl. Robotics) | Pattern Recognition | Mathematical Logic and Formal Languages | Algorithm Analysis and Problem Complexity | Computation by Abstract DevicesOnline resources: Click here to access online
Contents:
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.
In: Springer eBooks
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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.

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