Literatur zur Informationserschließung
Diese Datenbank enthält über 40.000 Dokumente zu Themen aus den Bereichen Formalerschließung – Inhaltserschließung – Information Retrieval.
© 2015 W. Gödert, TH Köln, Institut für Informationswissenschaft
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1Vidyasagar, M.: ¬A theory of learning and generalization : with applications to neural networks and control systems.
Berlin : Springer, 1997. XVIII,383 S.
ISBN 3-540-76120-9
(Communications and control engineering series)
Compass: Artificial intelligence
LCSH: Machine learning ; Neural networks (Computer science) ; Distribution (Probability theory)
RSWK: Neuronales Netz / Maschinelles Lernen (21) ; Regelungssystem / Maschinelles Lernen (21)
DDC: 006.31
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2Krause, P.J. ; Clark, D.: Representing uncertain knowledge.
Dordrecht : Kluwer, 1993. IX,277 S.
ISBN 0-7923-2433-1
Themenfeld: Information
Wissenschaftsfach: Wissenschaftstheorie
Compass: Artificial intelligence
LCSH: Uncertainty (Information theory) ; Artificial intelligence ; Knowledge representation (Information theory)
DDC: 006.3
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3Shapiro, S.C. (Hrsg.): Encyclopedia of artificial intelligence.2nd ed.
New York : Wiley, 1992. 2 vols. XX,1689 S.
ISBN 0-471-50307-X
Inhalt: Aktualisiert die erste Aufl. von 1987
Wissenschaftsfach: Informatik
Compass: Artificial intelligence
DDC: 006.303
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4Ford, N.: Expert systems and artificial intelligence : an information manager's guide.
London : Library Association, 1991. 304 S.
ISBN 0-85365-909-5
Abstract: A guide to database management, online searching, text retrieval, data analysis, intelligent documents and more ...
Anmerkung: Rez. in: Program 28(1994) no.2, S.211-212 (M. Malinconicao)
Wissenschaftsfach: Informatik
Compass: Artificial intelligence
DDC: 006.3
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5Hodgson, J.P.E.: Knowledge representation and language in AI.
Chichester, UK : Horwood, 1991. 230 S.
ISBN 0-13-515123-6
(Ellis Horwood series in artificial intelligence)
Abstract: The aim of this book is to highlight the relationship between knowledge representation and language in artificial intelligence, and in particular on the way in which the choice of representation influences the language used to discuss a problem - and vice versa. Opening with a discussion of knowledge representation methods, and following this with a look at reasoning methods, the author begins to make his case for the intimate relationship between language and representation. He shows how each representation method fits particularly well with some reasoning methods and less so with others, using specific languages as examples. The question of representation change, an important and complex issue about which very little is known, is addressed. Dr Hodgson gathers together recent work on problem solving, showing how, in some cases, it has been possible to use representation changes to recast problems into a language that makes them easier to solve. The author maintains throughout that the relationships that this book explores lie at the heart of the construction of large systems, examining a number of the current large AI systems from the viewpoint of representation and language to prove his point.
Themenfeld: Wissensrepräsentation ; Computerlinguistik
Wissenschaftsfach: Informatik
Compass: Artificial intelligence
LCSH: Knowledge / representation (Information theory) ; Computational linguistics ; Artificial intelligence
RSWK: Wissensrepräsentation / Künstliche Intelligenz (BVB) ; Künstliche Intelligenz / Programmiersprache (BVB) ; Wissensrepräsentation / Sprache (BVB)
BK: 54.72 / Künstliche Intelligenz
DDC: 006.3 ; 003'.54
GHBS: TVU (DU) ; HRH (PB)
LCC: Q387
RVK: ST 285 Informatik / Monographien / Software und -entwicklung / Computer supported cooperative work (CSCW), Groupware
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6Lenat, D.B. ; Guha, R.V.: Building large knowledge-based systems representation and inference in the CYC project.
Wokingham, UK : Addison-Wesley, 1990. XIX,372 S.
ISBN 0-201-51752-3
Anmerkung: Rez. in: New review of applied expert systems 2(1996) S.185-189 (J. Morris)
Wissenschaftsfach: Informatik
Objekt: Cyc
Compass: Artificial intelligence
DDC: 006.3