Machine learning : ECML-97 : 9th European Conference on Machine Learning Prague, Czech Republic, April 23 25, 1997 : proceedings

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...

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Dettagli Bibliografici
Autore principale: Van Someren, Maarten, 1955-
Ente Autore: European conference on machine learning (Autore)
Altri autori: Widmers, Gerhard, 1961- (Direttore editoriale)
Natura: Livre numérique
Lingua:Anglais
Pubblicazione: Berlin [etc.] : Springer [20..].
Cham : Springer Nature
Serie:Lecture notes in computer science. Lecture notes in artificial intelligence 1224
Soggetti:
Accesso online:Accès sur la plateforme de l'éditeur
Accès sur la plateforme Istex
Accès Université d'Orléans
Accès INSA CVL
Nota: Archives Springer e-books (Licence nationale)
Archives Springer e-books (Licence nationale)
Autres localisations: Voir dans le Sudoc
Edition sous un autre format:• Machine learning: ECML-97, 9th European Conference on Machine Learning, Prague, Czech Republic, April 1997, proceedings, Maarten van Someren, Gerhard Widmer (eds.), 1997, Berlin, Springer, 1 vol. (XI-360 p.), Lecture notes in computer science, 3-540-62858-4
• Machine Learning: ECML'97, Texte imprimé, 9783662203620
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505 0 |a 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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