LEADER 04155nam 22006015 450 001 996466130703316 005 20200702082638.0 010 $a3-540-44737-7 024 7 $a10.1007/3-540-60217-8 035 $a(CKB)1000000000234320 035 $a(SSID)ssj0000321171 035 $a(PQKBManifestationID)11212655 035 $a(PQKBTitleCode)TC0000321171 035 $a(PQKBWorkID)10260432 035 $a(PQKB)10973053 035 $a(DE-He213)978-3-540-44737-5 035 $a(PPN)155174770 035 $a(EXLCZ)991000000000234320 100 $a20121227d1995 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aAlgorithmic Learning for Knowledge-Based Systems$b[electronic resource] $eGOSLER Final Report /$fedited by Klaus P. Jantke, Steffen Lange 205 $a1st ed. 1995. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d1995. 215 $a1 online resource (X, 522 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v961 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-60217-8 327 $aLearning and consistency -- Error detecting in inductive inference -- Learning from good examples -- Towards reduction arguments for FINite learning -- Not-so-nearly-minimal-size program inference (preliminary report) -- Optimization problem in inductive inference -- On identification by teams and probabilistic machines -- Topological considerations in composing teams of learning machines -- Probabilistic versus deterministic memory limited learning -- Classification using information -- Classifying recursive predicates and languages -- A guided tour across the boundaries of learning recursive languages -- Pattern inference -- Inductive learning of recurrence-term languages from positive data -- Learning formal languages based on control sets -- Learning in case-based classification algorithms -- Optimal strategies ? Learning from examples ? Boolean equations -- Feature construction during tree learning -- On lower bounds for the depth of threshold circuits with weights from {?1,0,+1} -- Structuring neural networks and PAC-Learning -- Inductive synthesis of rewrite programs -- TLPS ? A term rewriting laboratory (not only) for experiments in automatic program synthesis -- GoslerP ? A logic programming tool for inductive inference. 330 $aThis book is the final report on a comprehensive basic research project, named GOSLER on algorithmic learning for knowledge-based systems supported by the German Federal Ministry of Research and Technology during the years 1991 - 1994. This research effort was focused on the study of fundamental learnability problems integrating theoretical research with the development of tools and experimental investigation. The contributions by 11 participants in the GOSLER project is complemented by contributions from 23 researchers from abroad. Thus the volume provides a competent introduction to algorithmic learning theory. 410 0$aLecture Notes in Artificial Intelligence ;$v961 606 $aComputers 606 $aArtificial intelligence 606 $aMathematical logic 606 $aTheory of Computation$3https://scigraph.springernature.com/ontologies/product-market-codes/I16005 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aMathematical Logic and Formal Languages$3https://scigraph.springernature.com/ontologies/product-market-codes/I16048 615 0$aComputers. 615 0$aArtificial intelligence. 615 0$aMathematical logic. 615 14$aTheory of Computation. 615 24$aArtificial Intelligence. 615 24$aMathematical Logic and Formal Languages. 676 $a006.3/3 702 $aJantke$b Klaus P$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLange$b Steffen$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a996466130703316 996 $aAlgorithmic learning for knowledge-based systems$91501912 997 $aUNISA