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  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.22 
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    Content
    Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.
    Date
    8. 1.2013 10:22:32
  2. Rötzer, F.: Psychologen für die Künstliche Intelligenz (2018) 0.21 
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    Abstract
    Künstliche Intelligenz kann lernen anhand von Erfahrungen und Vorgaben. Das Problem ist aber, dass sich maschinelles Lernen zwar in eine bestimmte Richtung steuern lässt, um bestimmte Probleme zu lösen, dass aber der Programmierer mitunter nicht mehr weiß, was in dem "künstlichen Gehirn" bzw. dem neuronalen Netzwerk vor sich geht, das mit zunehmender Komplexität und Lernerfahrung für den Menschen zur Black Box wird, wo man nur sehen kann, was an Daten einfließt und was herauskommt.
    Date
    22. 1.2018 11:08:27
  3. Schrodt, R.: Tiefen und Untiefen im wissenschaftlichen Sprachgebrauch (2008) 0.18 
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    Content
    Vgl. auch: https://studylibde.com/doc/13053640/richard-schrodt. Vgl. auch: http%3A%2F%2Fwww.univie.ac.at%2FGermanistik%2Fschrodt%2Fvorlesung%2Fwissenschaftssprache.doc&usg=AOvVaw1lDLDR6NFf1W0-oC9mEUJf.
  4. Popper, K.R.: Three worlds : the Tanner lecture on human values. Deliverd at the University of Michigan, April 7, 1978 (1978) 0.18 
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    Source
    https%3A%2F%2Ftannerlectures.utah.edu%2F_documents%2Fa-to-z%2Fp%2Fpopper80.pdf&usg=AOvVaw3f4QRTEH-OEBmoYr2J_c7H
  5. Li, L.; Shang, Y.; Zhang, W.: Improvement of HITS-based algorithms on Web documents 0.18 
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    Content
    Vgl.: http%3A%2F%2Fdelab.csd.auth.gr%2F~dimitris%2Fcourses%2Fir_spring06%2Fpage_rank_computing%2Fp527-li.pdf. Vgl. auch: http://www2002.org/CDROM/refereed/643/.
  6. Suchenwirth, L.: Sacherschliessung in Zeiten von Corona : neue Herausforderungen und Chancen (2019) 0.18 
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    Footnote
    https%3A%2F%2Fjournals.univie.ac.at%2Findex.php%2Fvoebm%2Farticle%2Fdownload%2F5332%2F5271%2F&usg=AOvVaw2yQdFGHlmOwVls7ANCpTii.
  7. Vetere, G.; Lenzerini, M.: Models for semantic interoperability in service-oriented architectures (2005) 0.16 
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    Content
    Vgl.: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5386707&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5386707.
  8. Mas, S.; Marleau, Y.: Proposition of a faceted classification model to support corporate information organization and digital records management (2009) 0.14 
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    Footnote
    Vgl.: http://ieeexplore.ieee.org/Xplore/login.jsp?reload=true&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel5%2F4755313%2F4755314%2F04755480.pdf%3Farnumber%3D4755480&authDecision=-203.
  9. Zeng, Q.; Yu, M.; Yu, W.; Xiong, J.; Shi, Y.; Jiang, M.: Faceted hierarchy : a new graph type to organize scientific concepts and a construction method (2019) 0.14 
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    Content
    Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.
  10. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.14 
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    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  11. Donsbach, W.: Wahrheit in den Medien : über den Sinn eines methodischen Objektivitätsbegriffes (2001) 0.12 
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    Source
    Politische Meinung. 381(2001) Nr.1, S.65-74 [https%3A%2F%2Fwww.dgfe.de%2Ffileadmin%2FOrdnerRedakteure%2FSektionen%2FSek02_AEW%2FKWF%2FPublikationen_Reihe_1989-2003%2FBand_17%2FBd_17_1994_355-406_A.pdf&usg=AOvVaw2KcbRsHy5UQ9QRIUyuOLNi]
  12. Malsburg, C. von der: ¬The correlation theory of brain function (1981) 0.12 
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    http%3A%2F%2Fcogprints.org%2F1380%2F1%2FvdM_correlation.pdf&usg=AOvVaw0g7DvZbQPb2U7dYb49b9v_
  13. Hoppe, A.: ¬Die systematischen Grundlagen für ein linguistisch orientiertes maschinelles Dokumentationsverfahren (1969) 0.11 
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  14. Lepsky, K.: Maschinelles Indexieren zur Verbesserung der sachlichen Suche im OPAC : DFG-Projekt an der Universitäts- und Landesbibliothek Düsseldorf (1994) 0.11 
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  15. Sarre, F.; Güntzer, U.; Myka, A.; Jüttner, G.: Maschinelles Lernen von Relationen für Thesauri und Hypertext (1992) 0.10 
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    Abstract
    Fortschrittliche Informationssysteme stellen ihren Benutzern 2 wichtige Suchmethoden zur Verfügung: die gezielte (Volltext-) Suche und das Navigieren im Objektbestand mit Hilfe von Hypertext-Links. Der Grund, warum diese beiden Konzepte aber auf breiter Basis noch nicht in jedem Informationssystem Anwendung gefunden haben, ist darin zu sehen, daß der manuelle Aufbau von umfassenden Hypertext-Strukturen auf der einen Seite und von großen Thesauri, die den Erfolg von Volltextsuchen wesentlich steigern, auf der anderen Seite bislang enormen Aufwand und damit hohe Kosten verursachte. Langfristig werden Informationssysteme aber nur dann große Akzeptanz bei der Benutzerschaft erzielen, wenn sie ihre Benutzer mit diesen beiden Techniken unterstützen und wenn sie dynamisch neuen Informationsbedürfnissen anpassen können, also lernfähig sind. Für den einzelnen Benutzer ergibt sich daraus der wesentliche Vorteil, daß er von den Recherche-Erfahrungen anderer Benutzer profitieren kann. In diesem Papier stellen wir eine Lernkomponente vor, die für das Hypertextsystem 'HyperMan' an der TU München entwickelt und implementiert wurde. Wir zeigen beispielhaft, wie Volltext-Suchanfragen der HyperMan-Benutzer von der Lernkomponente untersucht werden, um Thesauruseinträge zu gewinnen. Bei der Entwicklung dieser Lerntechniken zum (automatischen) Thesaurusaufbau konnte auf Erfahrungen mit dem lernfähigen Information Retrieval System 'Tegen' zurückgegriffen werden. In dem HyperMan System werden aber nicht nur Beziehungen (Relationen) zwischen Begriffen erlernt, sondern auch zwischen Textstücken. Wir gehen daher auch darauf ein, wie aufgrund einer Analyse des Benutzerverhaltens sowohl neue Hypertext-Links erlernt als auch vorhandene Links, die zuvor von HyperMans Generierungskomponente automatisch erzeugt wurden, modifiziert werden
  16. Lepsky, K.: Automatisierung in der Sacherschließung : Maschinelles Indexieren von Titeldaten (1996) 0.09 
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  17. Axelos, C.; Flasch, K.; Schepers, H.; Kuhlen, R.; Romberg, R.; Zimmermann, R.: Allgemeines/Besonderes (1971-2007) 0.09 
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    Footnote
    DOI: 10.24894/HWPh.5033. Vgl. unter: https://www.schwabeonline.ch/schwabe-xaveropp/elibrary/start.xav#__elibrary__%2F%2F*%5B%40attr_id%3D%27verw.allgemeinesbesonderes%27%5D__1515856414979.
  18. Logothetis, N.K.: ¬Das Sehen : ein Fenster zum Bewußtsein (2000) 0.08 
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    Abstract
    Wie merken wir, was wir sehen? Die Hirnvorgänge beim Wahrnehmen doppeldeutiger Testbilder eröffnen faszinierende Einblicke in das Wesen geistiger Prozesse
    Content
    Mehrere Bilder: (1) Dahli, 3 Gesichter ? (2) Necker-Würfel (3) Sehen -> Wahrnehmung
    Date
    22. 7.2000 19:02:07
  19. Schmid, U.: Aus Erfahrung schlau : Maschinelles Lernen (2002) 0.08 
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  20. Binder, A.; Meinecke, F.C.; Bießmann, F.; Kawanabe, M.; Müller, K.-R.: Maschinelles Lernen, Mustererkennung in der Bildverarbeitung (2013) 0.08 
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