Computational Learning Theory - neues Buch

EAN (ISBN-13): 9783540591191
ISBN (ISBN-10): 3540591192
Taschenbuch
Erscheinungsjahr: 1995
Herausgeber: Springer Berlin Heidelberg
440 Seiten
Gewicht: 0,661 kg
Sprache: eng/Englisch

Buch in der Datenbank seit 2007-10-04T16:34:36+02:00 (Vienna)
Detailseite zuletzt geändert am 2024-01-31T14:35:49+01:00 (Vienna)
ISBN/EAN: 9783540591191

ISBN - alternative Schreibweisen:
3-540-59119-2, 978-3-540-59119-1
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: paul
Titel des Buches: computational intelligence, european conference artificial intelligence, proceedings artificial intelligence conference, lecture notes artificial intelligence, barcelona, 1995

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Daten vom Verlag:

Autor/in: Paul Vitanyi
Titel: Lecture Notes in Artificial Intelligence; Lecture Notes in Computer Science; Computational Learning Theory - Second European Conference, EuroCOLT '95, Barcelona, Spain, March 13 - 15, 1995. Proceedings
Verlag: Springer; Springer Berlin
422 Seiten
Erscheinungsjahr: 1995-02-23
Berlin; Heidelberg; DE
Sprache: Englisch
53,49 € (DE)
54,99 € (AT)
59,00 CHF (CH)
Available
XVIII, 422 p.

BC; Hardcover, Softcover / Informatik, EDV/Informatik; Künstliche Intelligenz; Verstehen; Algorithms; algorithm; evolutionary algorithm; learning; learning theory; logic; machine learning; programming; robot; robotics; algorithm analysis and problem complexity; Artificial Intelligence; Formal Languages and Automata Theory; Algorithms; Theoretische Informatik; Algorithmen und Datenstrukturen; EA

The discovery of algorithmic probability: A guide for the programming of true creativity.- A desicion-theoretic generalization of on-line learning and an application to boosting.- Online learning versus offline learning.- Learning distributions by their density levels — A paradigm for learning without a teacher.- Tight worst-case loss bounds for predicting with expert advice.- On-line maximum likelihood prediction with respect to general loss functions.- The power of procrastination in inductive inference: How it depends on used ordinal notations.- Learnability of Kolmogorov-easy circuit expressions via queries.- Trading monotonicity demands versus mind changes.- Learning recursive functions from approximations.- On the intrinsic complexity of learning.- The structure of intrinsic complexity of learning.- Kolmogorov numberings and minimal identification.- Stochastic complexity in learning.- Function learning from interpolation (extended abstract).- Approximation and learning of convex superpositions.- Minimum description length estimators under the optimal coding scheme.- MDL learning of unions of simple pattern languages from positive examples.- A note on the use of probabilities by mechanical learners.- Characterizing rational versus exponential learning curves.- Is pocket algorithm optimal?.- Some theorems concerning the free energy of (Un) constrained stochastic Hopfield neural networks.- A space-bounded learning algorithm for axis-parallel rectangles.- Learning decision lists and trees with equivalence-queries.- Bounding VC-dimension for neural networks: Progress and prospects.- Average case analysis of a learning algorithm for ?-DNF expressions.- Learning by extended statistical queries and its relation to PAC learning.- Typed pattern languages and theirlearnability.- Learning behaviors of automata from shortest counterexamples.- Learning of regular expressions by pattern matching.- The query complexity of learning some subclasses of context-free grammars.