LEADER 03699nam 22004935 450 001 996465318903316 005 20200702024431.0 010 $a3-540-46081-0 024 7 $a10.1007/BFb0017213 035 $a(CKB)1000000000233357 035 $a(SSID)ssj0000324265 035 $a(PQKBManifestationID)11273763 035 $a(PQKBTitleCode)TC0000324265 035 $a(PQKBWorkID)10304221 035 $a(PQKB)11013948 035 $a(DE-He213)978-3-540-46081-7 035 $a(PPN)155217267 035 $a(EXLCZ)991000000000233357 100 $a20121227d1989 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aKnowledge Representation and Organization in Machine Learning$b[electronic resource] /$fedited by Katharina Morik 205 $a1st ed. 1989. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d1989. 215 $a1 online resource (XVIII, 322 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v347 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-50768-X 327 $aExplanation: A source of guidance for knowledge representation -- (Re)presentation issues in second generation expert systems -- Some aspects of learning and reorganization in an analogical representation -- A knowledge-intensive learning system for document retrieval -- Constructing expert systems as building mental models or toward a cognitive ontology for expert systems -- Sloppy modeling -- The central role of explanations in disciple -- An inference engine for representing multiple theories -- The acquisition of model-knowledge for a model-driven machine learning approach -- Using attribute dependencies for rule learning -- Learning disjunctive concepts -- The use of analogy in incremental SBL -- Knowledge base refinement using apprenticeship learning techniques -- Creating high level knowledge structures from simple elements -- Demand-driven concept formation. 330 $aMachine learning has become a rapidly growing field of Artificial Intelligence. Since the First International Workshop on Machine Learning in 1980, the number of scientists working in the field has been increasing steadily. This situation allows for specialization within the field. There are two types of specialization: on subfields or, orthogonal to them, on special subjects of interest. This book follows the thematic orientation. It contains research papers, each of which throws light upon the relation between knowledge representation, knowledge acquisition and machine learning from a different angle. Building up appropriate representations is considered to be the main concern of knowledge acquisition for knowledge-based systems throughout the book. Here machine learning is presented as a tool for building up such representations. But machine learning itself also states new representational problems. This book gives an easy-to-understand insight into a new field with its problems and the solutions it offers. Thus it will be of good use to both experts and newcomers to the subject. 410 0$aLecture Notes in Artificial Intelligence ;$v347 606 $aArtificial intelligence 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aArtificial intelligence. 615 14$aArtificial Intelligence. 676 $a006.3 702 $aMorik$b Katharina$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a996465318903316 996 $aKnowledge Representation and Organization in Machine Learning$92831690 997 $aUNISA