Computational Learning Theory
% SALE
Computational Learning Theory
% SALE
% SALE
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.
Product details
- Type:
- Book
- Binding:
- Paperback
- Edition:
- 1
- Languages:
-
Original:
English
- Number of Pages:
-
324
- Publisher:
-
Springer
- Publication Date:
-
1999-03-17
- ISBN / EAN:
-
9783540657019
- Weight:
-
493 g
- Dimensions:
-
16/24/2 cm
- GPSR Manufacturer Reference:
-
[email protected]
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