Computational Learning Theory: 4th European Conference, EuroCOLT'99 Nordkirchen, Germany, March 29-31, 1999 Proceedings (Lecture Notes in Computer Science, 1572, Band 1572) - Taschenbuch

EAN (ISBN-13): 9783540657019
ISBN (ISBN-10): 3540657010
Gebundene Ausgabe
Taschenbuch
Erscheinungsjahr: 1999
Herausgeber: Springer
324 Seiten
Gewicht: 0,491 kg
Sprache: eng/Englisch

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ISBN/EAN: 9783540657019

ISBN - alternative Schreibweisen:
3-540-65701-0, 978-3-540-65701-9
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: fischer hans, hans paul, ulrich fischer, paul simon, simo, hans see, fisch paul
Titel des Buches: nordkirchen, european conference artificial intelligence, the sciences the artificial, lectures, proceedings the third european conference, who who 1999, march, how get all you want when germany, science the artificial, lecture notes artificial intelligence, com, computational, learning, theory, lecture notes computer science, paul fischer

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Daten vom Verlag:

Autor/in: Paul Fischer; Hans U. Simon
Titel: Lecture Notes in Artificial Intelligence; Lecture Notes in Computer Science; Computational Learning Theory - 4th European Conference, EuroCOLT'99 Nordkirchen, Germany, March 29-31, 1999 Proceedings
Verlag: Springer; Springer Berlin
299 Seiten
Erscheinungsjahr: 1999-03-17
Berlin; Heidelberg; DE
Sprache: Englisch
53,49 € (DE)
54,99 € (AT)
59,00 CHF (CH)
Available
X, 299 p.

BC; Hardcover, Softcover / Informatik, EDV/Informatik; Künstliche Intelligenz; Verstehen; Algorithmic Learning; Computational Learning; Inductive Inference; Online Learning; learning; learning theory; pattern recognition; reinforcement learning; algorithm analysis and problem complexity; Artificial Intelligence; Automated Pattern Recognition; Formal Languages and Automata Theory; Algorithms; Theory of Computation; Mustererkennung; Theoretische Informatik; Algorithmen und Datenstrukturen; EA

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.
Includes supplementary material: sn.pub/extras