Machine Learning: ECML'97 - neues Buch

EAN (ISBN-13): 9783540687085
Herausgeber: Springer Science+Business Media

Buch in der Datenbank seit 2017-05-05T15:35:30+02:00 (Berlin)
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ISBN/EAN: 9783540687085

ISBN - alternative Schreibweisen:
978-3-540-68708-5
Alternative Schreibweisen und verwandte Suchbegriffe:
Titel des Buches: machine learning

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Daten vom Verlag:

Autor/in: Maarten van Someren; Gerhard Widmer
Titel: Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence; Machine Learning: ECML'97 - 9th European Conference on Machine Learning, Prague, Czech Republic, April 23 - 25, 1997, Proceedings
Verlag: Springer; Springer Berlin
366 Seiten
Erscheinungsjahr: 2005-07-07
Berlin; Heidelberg; DE
Sprache: Englisch
53,49 € (DE)
55,00 € (AT)
59,00 CHF (CH)
Available
XIV, 366 p.

EA; E107; eBook; Nonbooks, PBS / Informatik, EDV/Informatik; Künstliche Intelligenz; Verstehen; Algorithmisches lernen; Entscheidungstheorie; Induktives Logisches Programmieren; Lernende Agenten; Maschinelles Lernen; algorithmic learning; classification; decision making; genetic programming; inductive logic programming; learning; logic; machine learning; programming; reinforcement learning; algorithm analysis and problem complexity; C; Artificial Intelligence; Algorithms; Computer Science; Algorithmen und Datenstrukturen; BC

Uncertain learning agents.- Constructing and sharing perceptual distinctions.- On prediction by data compression.- Induction of feature terms with INDIE.- Exploiting qualitative knowledge to enhance skill acquisition.- Integrated learning and planning based on truncating temporal differences.- ?-subsumption for structural matching.- Classification by Voting Feature Intervals.- Constructing intermediate concepts by decomposition of real functions.- Conditions for Occam's razor applicability and noise elimination.- Learning different types of new attributes by combining the neural network and iterative attribute construction.- Metrics on terms and clauses.- Learning when negative examples abound.- A model for generalization based on confirmatory induction.- Learning Linear Constraints in Inductive Logic Programming.- Finite-Element methods with local triangulation refinement for continuous reinforcement learning problems.- Inductive Genetic Programming with Decision Trees.- Parallel anddistributed search for structure in multivariate time series.- Compression-based pruning of decision lists.- Probabilistic Incremental Program Evolution: Stochastic search through program space.- NeuroLinear: A system for extracting oblique decision rules from neural networks.- Inducing and using decision rules in the GRG knowledge discovery system.- Learning and exploitation do not conflict under minimax optimality.- Model combination in the multiple-data-batches scenario.- Search-based class discretization.- Natural ideal operators in Inductive Logic Programming.- A case study in loyalty and satisfaction research.- Ibots learn genuine team solutions.- Global data analysis and the fragmentation problem in decision tree induction.- Case-based learning: Beyond classification of feature vectors.- Empirical learning of Natural Language Processing tasks.- Human-Agent Interaction and Machine Learning.- Learning in dynamically changing domains: Theory revision and context dependence issues.