LEADER 04632nam 22005655 450 001 9910768189203321 005 20240807224834.0 010 $a3-540-68708-4 024 7 $a10.1007/3-540-62858-4 035 $a(CKB)1000000000234630 035 $a(SSID)ssj0000324586 035 $a(PQKBManifestationID)11241183 035 $a(PQKBTitleCode)TC0000324586 035 $a(PQKBWorkID)10313464 035 $a(PQKB)11510173 035 $a(DE-He213)978-3-540-68708-5 035 $a(PPN)155212842 035 $a(EXLCZ)991000000000234630 100 $a20121227d1997 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aMachine Learning: ECML'97 $e9th European Conference on Machine Learning, Prague, Czech Republic, April 23 - 25, 1997, Proceedings /$fedited by Maarten van Someren, Gerhard Widmer 205 $a1st ed. 1997. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d1997. 215 $a1 online resource (XIV, 366 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v1224 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-62858-4 327 $aUncertain 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 and distributed 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. 330 $aThis book constitutes the refereed proceedings of the Ninth European Conference on Machine Learning, ECML-97, held in Prague, Czech Republic, in April 1997. This volume presents 26 revised full papers selected from a total of 73 submissions. Also included are an abstract and two papers corresponding to the invited talks as well as descriptions from four satellite workshops. The volume covers the whole spectrum of current machine learning issues. 410 0$aLecture Notes in Artificial Intelligence ;$v1224 606 $aArtificial intelligence 606 $aAlgorithms 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 615 0$aArtificial intelligence. 615 0$aAlgorithms. 615 14$aArtificial Intelligence. 615 24$aAlgorithm Analysis and Problem Complexity. 676 $a006.3/1 702 $aSomeren$b Maarten W. van$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aWidmer$b Gerhard$f1961-$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aEuropean Conference on Machine Learning 906 $aBOOK 912 $a9910768189203321 996 $aMachine Learning: ECML'97$92035797 997 $aUNINA