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Maarten van Someren#Gerhard Widmer:

Machine Learning: ECML'97 - Livres de poche

1997, ISBN: 9783540628583

This book constitutes the refereed proceedings of the Ninth European Conference on Machine Learning, ECML-97, held in Prague, Czech Republic, in April 1997. This volume presents 26 rev… Plus…

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Maarten van Someren; Gerhard Widmer:

Machine Learning: ECML'97 - Livres de poche

ISBN: 9783540628583

This book constitutes the refereed proceedings of the Ninth European Conference on Machine Learning, ECML-97, held in Prague, Czech Republic, in April 1997. This volume presents 26 revise… Plus…

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Maarten van Someren:
Machine Learning: ECML'97 - Livres de poche

ISBN: 9783540628583

Paperback, [PU: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG], This book constitutes the refereed proceedings of the Ninth European Conference on Machine Learning, ECML-97, held in… Plus…

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Someren, Maarten van [Editor]; Widmer, Gerhard [Editor];:
Machine Learning: ECML'97: 9th European Conference on Machine Learning, Prague, Czech Republic, April 23 - 25, 1997, Proceedings (Lecture Notes in ... / Lecture Notes in Artificial Intelligence) - Livres de poche

1997, ISBN: 9783540628583

Springer, 1997-05-16. Paperback. Very Good. Ex-library paperback in very nice condition with the usual markings and attachments., Springer, 1997-05-16, 3

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Machine Learning: ECML'97 - Livres de poche

ISBN: 9783540628583

*Machine Learning: ECML'97* - 9th European Conference on Machine Learning Prague Czech Republic April 23 - 25 1997 Proceedings. Auflage 1997 / Taschenbuch für 53.49 € / Aus dem Bereich: B… Plus…

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Machine Learning: ECML'97
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ISBN:

3540628584


This book constitutes the refereed proceedings of the Ninth European Conference on Machine Learning, ECML-97, held in Prague, Czech Republic, in April 1997.This volume presents 26 revised full papers selected from a total of 73 submissions. Also included are an abstract and two papers corresponding to the invited talks as well as descriptions from four satellite workshops. The volume covers the whole spectrum of current machine learning issues.

Informations détaillées sur le livre - Machine Learning: ECML'97


EAN (ISBN-13): 9783540628583
ISBN (ISBN-10): 3540628584
Livre de poche
Date de parution: 1997
Editeur: Springer Berlin Heidelberg
380 Pages
Poids: 0,573 kg
Langue: eng/Englisch

Livre dans la base de données depuis 2007-06-06T01:50:00+02:00 (Zurich)
Page de détail modifiée en dernier sur 2024-02-12T13:09:54+01:00 (Zurich)
ISBN/EAN: 3540628584

ISBN - Autres types d'écriture:
3-540-62858-4, 978-3-540-62858-3
Autres types d'écriture et termes associés:
Auteur du livre: someren, widmer
Titre du livre: czech republic, machine learning, republic com, european conference artificial intelligence, prague then and now, ecm

Données de l'éditeur

Auteur: Maarten van Someren; Gerhard Widmer
Titre: 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
Editeur: Springer; Springer Berlin
366 Pages
Date de parution: 1997-04-09
Berlin; Heidelberg; DE
Langue: Anglais
53,49 € (DE)
54,99 € (AT)
59,00 CHF (CH)
Available
XIV, 366 p.

BC; Hardcover, Softcover / 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; Artificial Intelligence; Algorithms; Algorithmen und Datenstrukturen; EA

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.
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