ISBN: 9783540490975
This volume contains papers presented at the Fourth European Conference on ComputationalLearningTheory,whichwasheldatNordkirchenCastle,inNo- kirchen, NRW, Germany, from March 29 to 31, 19… Mehr…
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ISBN: 9783540490975
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ISBN: 9783540490975
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ISBN: 9783540490975
This volume contains papers presented at the Fourth European Conference on ComputationalLearningTheory,whichwasheldatNordkirchenCastle,inNo- kirchen, NRW, Germany, from March 29 to 31, 19… Mehr…
2003
ISBN: 9783540490975
4th European Conference, EuroCOLT'99 Nordkirchen, Germany, March 29-31, 1999 Proceedings, eBooks, eBook Download (PDF), [PU: Springer Berlin Heidelberg], Springer Berlin Heidelberg, 2003
ISBN: 9783540490975
Computational Learning Theory ab 71.49 EURO 4th European Conference, EuroCOLT'99 Nordkirchen, Germany, March 29-31, 1999 Proceedings Medien > Bücher, [PU: Springer, Berlin/Heidelberg]
ISBN: 9783540490975
There is currently no description available Books > Computer Science eBook, Springer Shop
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Detailangaben zum Buch - Computational Learning Theory
EAN (ISBN-13): 9783540490975
Erscheinungsjahr: 2003
Herausgeber: Springer Berlin Heidelberg
Buch in der Datenbank seit 2017-02-21T19:48:33+01:00 (Zurich)
Detailseite zuletzt geändert am 2023-09-22T11:59:35+02:00 (Zurich)
ISBN/EAN: 9783540490975
ISBN - alternative Schreibweisen:
978-3-540-49097-5
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: fischer hans, simon hans, paul simon, norbert straumann
Titel des Buches: learning, nordkirchen
Daten vom Verlag:
Autor/in: Paul Fischer; Hans U. Simon
Titel: Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence; Computational Learning Theory - 4th European Conference, EuroCOLT'99 Nordkirchen, Germany, March 29-31, 1999 Proceedings
Verlag: Springer; Springer Berlin
299 Seiten
Erscheinungsjahr: 2003-07-31
Berlin; Heidelberg; DE
Sprache: Englisch
53,49 € (DE)
55,00 € (AT)
59,00 CHF (CH)
Available
X, 299 p.
EA; E107; eBook; Nonbooks, PBS / 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; C; Artificial Intelligence; Automated Pattern Recognition; Formal Languages and Automata Theory; Algorithms; Theory of Computation; Computer Science; Mustererkennung; Theoretische Informatik; Algorithmen und Datenstrukturen; BC
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
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