MARC Record from marc_columbia

LEADER: 04175mam a2200385 a 4500
001 1703395
005 20220608214550.0
008 951107t19951995enka b 001 0 eng d
020 $a0126851204
035 $a(OCoLC)33500330
035 $a(OCoLC)ocm33500330
035 $a(CStRLIN)NJRG95-B31904
035 $9ALA4140CU
035 $a(NNC)1703395
035 $a1703395
040 $aNjR$cNjR$dOrLoB
245 00 $aMachine learning and knowledge acquisition :$bintegrated approaches /$cedited by Gheorghe Tecuci, Yves Kodratoff.
260 $aLondon ;$aSan Diego :$bAcademic Press,$c[1995], ©1995.
300 $avi, 325 pages :$billustrations ;$c24 cm.
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
490 1 $aKnowledge-based systems book series ;$v14
504 $aIncludes bibliographical references and indexes.
505 00 $tIntroduction /$rGheorghe Tecuci and Yves Kodratoff --$g1.$tBuilding knowledge bases through multistrategy learning and knowledge acquisition /$rGheorghe Tecuci --$g2.$tIncreasing levels of assistance in refinement of knowledge-based retrieval systems /$rCatherine Baudin, Smadar Kedar and Barney Pell --$g3.$tTowards method-independent knowledge acquisition /$rYolanda Gil and Cecile Paris --$g4.$tAn integrated framework for knowledge representation and theory revision /$rMoshe Koppel, Albert Maria Segre and Ronen Feldman --$g5.$tFrom balanced cooperative modelling to embedded adaptivity: using inductive logic programming techniques for knowledge acquisition /$rStefan Wrobel --$g6.$tCooperative knowledge evolution for complex domains /$rFranz Schmalhofer and Bidjan Tschaitschian --$g7.$tA machine learning approach to knowledge-based software engineering /$rChristopher A. Miller and Keith R. Levi --$g8.$tKnowledge acquisition and learning by experience - the role of case-specific knowledge /$rAgnar Aamodt --
505 80 $g9.$tLearning control knowledge through case-based acquisition of user optimization preferences in ill-structured domains /$rKatia Sycara and Kazuo Miyashita --$g10.$tIndustrial applications of machine learning: illustrations for the knowledge acquisition-machine learning dilemma and for situated case-based reasoning /$rYves Kodratoff and Herve Mignot.
520 $aMachine learning and knowledge acquisition represent two complementary approaches to the acquisition and organization of knowledge for knowledge-based systems. Machine learning has focused on developing autonomous algorithms for acquiring knowledge as data and for knowledge compilation and organization. In contrast, knowledge acquisition has focused on improving and partially automating the acquisition of knowledge from human experts by knowledge engineers.
520 8 $aCurrently, both fields are moving towards an integrated approach using machine learning techniques to automate knowledge acquisition from experts, and knowledge acquisition techniques to guide and assist the learning process.
520 8 $aThis is the first book to present some of the most representative approaches to the integration of machine learning and knowledge acquisition such as case-based reasoning, apprenticeship learning, knowledge base refinement through multistrategy learning, example-guided knowledge based revision, and interactive inductive logic programming. It also presents their application to such areas as planning, scheduling, diagnosis, control, information retrieval and robotics.
520 8 $aThe book's tutorial style and description of real-world applications will make it essential reading for students, researchers and practitioners working in machine learning and knowledge acquisition.
650 0 $aMachine learning.$0http://id.loc.gov/authorities/subjects/sh85079324
650 0 $aKnowledge acquisition (Expert systems)$0http://id.loc.gov/authorities/subjects/sh89003634
700 1 $aTecuci, Gheorghe.$0http://id.loc.gov/authorities/names/n93102267
700 1 $aKodratoff, Yves.$0http://id.loc.gov/authorities/names/n82213519
830 0 $aKnowledge-based systems (London, England) ;$vv. 14.$0http://id.loc.gov/authorities/names/n88508569
852 00 $boff,eng$hQA76.76.E95$iM33 1995g