Computational Learning Theory [electronic resource] : 4th European Conference, EuroCOLT’99 Nordkirchen, Germany, March 29–31, 1999 Proceedings / edited by Paul Fischer, Hans Ulrich Simon.
Material type:
TextSeries: Lecture Notes in Computer Science ; 1572Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 1999Description: X, 299 p. online resourceContent type: - text
- computer
- online resource
- 9783540490975
- 006.3 23
- Q334-342
- TJ210.2-211.495
E-BOOKS
| Home library | Call number | Materials specified | URL | Status | Date due | Barcode | |
|---|---|---|---|---|---|---|---|
| IMSc Library | Link to resource | Available | EBK6657 |
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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