MARC record from Internet Archive

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020 $a9783540688563
020 $a3540688560
020 $a3540200401
020 $a9783540200406
020 $z9783540200406
024 7 $a10.1007/978-3-540-68856-3$2doi
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037 $a978-3-540-20040-6$bSpringer$nhttp://www.springerlink.com
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100 1 $aRaedt, Luc de,$d1964-
245 10 $aLogical and relational learning /$cLuc De Raedt.
260 $aBerlin :$bSpringer,$c©2008.
300 $a1 online resource (xv, 387 pages) :$billustrations
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
347 $atext file
347 $bPDF
490 1 $aCognitive technologies,$x1611-2482
504 $aIncludes bibliographical references (pages 351-373) and indexes.
588 0 $aPrint version record.
505 0 $aAn Introduction to Logic -- An Introduction to Learning and Search -- Representations for Mining and Learning -- Generality and Logical Entailment -- The Upgrading Story -- Inducing Theories -- Probabilistic Logic Learning -- Kernels and Distances for Structured Data -- Computational Aspects of Logical and Relational Learning -- Lessons Learned.
520 $aThis textbook covers logical and relational learning in depth, and hence provides an introduction to inductive logic programming (ILP), multirelational data mining (MRDM) and (statistical) relational learning (SRL). These subfields of data mining and machine learning are concerned with the analysis of complex and structured data sets that arise in numerous applications, such as bio- and chemoinformatics, network analysis, Web mining, natural language processing, within the rich representations offered by relational databases and computational logic. The author introduces the machine learning and representational foundations of the field and explains some important techniques in detail by using some of the classic case studies centered around well-known logical and relational systems. The book is suitable for use in graduate courses and should be of interest to graduate students and researchers in computer science, databases and artificial intelligence, as well as practitioners of data mining and machine learning. It contains numerous figures and exercises, and slides are available for many chapters.
546 $aEnglish.
650 0 $aMachine learning.
650 0 $aData mining.
650 0 $aLogic programming.
650 0 $aRelational databases.
650 2 $aData Mining
650 6 $aApprentissage automatique.
650 6 $aExploration de données (Informatique)
650 6 $aProgrammation logique.
650 6 $aBases de données relationnelles.
650 7 $aInformatique.$2eclas
650 7 $aData mining.$2fast$0(OCoLC)fst00887946
650 7 $aLogic programming.$2fast$0(OCoLC)fst01002056
650 7 $aMachine learning.$2fast$0(OCoLC)fst01004795
650 7 $aRelational databases.$2fast$0(OCoLC)fst01093575
653 00 $ainformatieontsluiting
653 00 $ainformation retrieval
653 00 $ainformatieopslag
653 00 $ainformation storage
653 00 $ainformatiesystemen
653 00 $ainformation systems
653 00 $ainternet
653 00 $acomputerwetenschappen
653 00 $acomputer sciences
653 00 $akunstmatige intelligentie
653 00 $aartificial intelligence
653 00 $adatamining
653 00 $adata mining
653 00 $adatabasebeheer
653 00 $adatabase management
653 10 $aInformation and Communication Technology (General)
653 10 $aInformatie- en communicatietechnologie (algemeen)
655 4 $aElectronic books.
776 08 $iPrint version:$aRaedt, Luc de, 1964-$tLogical and relational learning.$dBerlin : Springer, ©2008$z9783540200406$z3540200401$w(OCoLC)226976498
830 0 $aCognitive technologies.
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856 40 $3SpringerLink$uhttps://doi.org/10.1007/978-3-540-68856-3
856 40 $3SpringerLink$uhttps://link.springer.com/book/10.1007/978-3-540-20040-6
856 40 $3SpringerLink$uhttps://link.springer.com/book/10.1007%2F978-3-540-68856-3
856 40 $3SpringerLink$uhttps://link.springer.com/book/10.1007/978-3-540-68856-3
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