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  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.30 
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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. Li, L.; Shang, Y.; Zhang, W.: Improvement of HITS-based algorithms on Web documents 0.29 
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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/.
  3. Suchenwirth, L.: Sacherschliessung in Zeiten von Corona : neue Herausforderungen und Chancen (2019) 0.29 
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    Footnote
    https%3A%2F%2Fjournals.univie.ac.at%2Findex.php%2Fvoebm%2Farticle%2Fdownload%2F5332%2F5271%2F&usg=AOvVaw2yQdFGHlmOwVls7ANCpTii.
  4. Schrodt, R.: Tiefen und Untiefen im wissenschaftlichen Sprachgebrauch (2008) 0.29 
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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.
  5. Popper, K.R.: Three worlds : the Tanner lecture on human values. Deliverd at the University of Michigan, April 7, 1978 (1978) 0.29 
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    Source
    https%3A%2F%2Ftannerlectures.utah.edu%2F_documents%2Fa-to-z%2Fp%2Fpopper80.pdf&usg=AOvVaw3f4QRTEH-OEBmoYr2J_c7H
  6. Donsbach, W.: Wahrheit in den Medien : über den Sinn eines methodischen Objektivitätsbegriffes (2001) 0.26 
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    Abstract
    Das Problem der Wahrnehmung und Darstellung von Wahrheit durch die Medien führt zu vier zentralen Fragen: Wie viel Wahrheit gibt es in der Welt, über die Journalisten berichten müssen? Wie ermittelt oder recherchiert man diese Wahrheit? Wie trennt man die Spreu vom Weizen? Und wie geht man als Journalist mit dem um, was man als Wahrheit erkannt hat oder erkannt zu haben glaubt? Hier gibt es ganz offensichtlich eine Parallele zwischen Journalisten und Wissenschaftlern. Journalisten und Wissenschaftler brauchen erstens Hypothesen, zweitens geeignete Hypothesentests, drittens ein gutes Abgrenzungs-Kriterium und viertens Verfahren, um die erkannten Sachverhalte auf angemessene Weise für eine Kommunikation mit anderen zu repräsentieren, das heißt sie darzustellen. Es gibt zwei große Unterschiede zwischen Journalisten und Wissenschaftlern: Journalisten sind in der Regel auf raum-zeitlich begrenzte Aussagen aus, Wissenschaftler in der Regel auf raumzeitlich unbegrenzte Gesetze. Aber diese Unterschiede sind fließend, weil Wissenschaftler raum-zeitlich begrenzte Aussagen brauchen, um ihre All-Aussagen zu überprüfen, und Journalisten sich immer häufiger auf das Feld der allgemeinen Gesetzes-Aussagen wagen oder doch zumindest Kausalinterpretationen für soziale Phänomene anbieten. Der zweite Unterschied besteht darin, dass die Wissenschaft weitgehend professionalisiert ist (zumindest gilt dies uneingeschränkt für die Naturwissenschaften und die Medizin), was ihr relativ klare Abgrenzungs- und Güte-Kriterien beschert hat. Diese fehlen weitgehend im Journalismus.
    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]
  7. Vetere, G.; Lenzerini, M.: Models for semantic interoperability in service-oriented architectures (2005) 0.25 
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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) 0.22 
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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) 0.22 
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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) 0.22 
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    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  11. Axelos, C.; Flasch, K.; Schepers, H.; Kuhlen, R.; Romberg, R.; Zimmermann, R.: Allgemeines/Besonderes (1971-2007) 0.20 
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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.
  12. Malsburg, C. von der: ¬The correlation theory of brain function (1981) 0.18 
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    Source
    http%3A%2F%2Fcogprints.org%2F1380%2F1%2FvdM_correlation.pdf&usg=AOvVaw0g7DvZbQPb2U7dYb49b9v_
  13. Herb, U.; Beucke, D.: ¬Die Zukunft der Impact-Messung : Social Media, Nutzung und Zitate im World Wide Web (2013) 0.16 
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    Content
    Vgl. unter: https://www.leibniz-science20.de%2Fforschung%2Fprojekte%2Faltmetrics-in-verschiedenen-wissenschaftsdisziplinen%2F&ei=2jTgVaaXGcK4Udj1qdgB&usg=AFQjCNFOPdONj4RKBDf9YDJOLuz3lkGYlg&sig2=5YI3KWIGxBmk5_kv0P_8iQ.
  14. Krämer, S.: Künstliche Intelligenz : der Aufstieg des Computers zum Modellbaukasten des Geistes (1994) 0.09 
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    Abstract
    Die Interpreten Künstlicher Intelligenz sind konfrontiert mit einem Paradoxon. Die praktischen Wirkungen Künstlicher Intelligenz sind minimal. Doch theoretisch betrachtet entfaltet die Künstliche Intelligenz eine ungebrochene Attraktivität und füllt die Feuilleton-Seiten ebenso zugkräftig wie die philosophischen Seminare. Die Künstliche Intelligenz - diese Schlußfolgerung drängt sich auf - wirkt weniger als eine Technik, sondern als eine Idee. Nicht ihre faktische Ingenieurleistung zählt, sondern das ihr zugrundeliegende Modell vom Geist
    Source
    Information Philosophie. 22(1994) H.2, S.16-28
  15. Singer, W.: Hirnentwicklung oder die Suche nach Kohärenz (1994) 0.08 
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    Date
    22. 7.2000 18:43:51
    Source
    Geist - Gehirn - Künstliche Intelligenz: zeitgenössische Modelle des Denkens. Ringvorlesung an der Freien Universität. Hrsg.: S. Krämer
  16. Friederici, A.: Gehirn und Sprache : Neurobiologische Grundlagen der Sprachverarbeitung (1994) 0.08 
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    Date
    22. 7.2000 18:43:38
    Source
    Geist - Gehirn - Künstliche Intelligenz: zeitgenössische Modelle des Denkens. Ringvorlesung an der Freien Universität. Hrsg.: S. Krämer
  17. ¬Der Student aus dem Computer (2023) 0.08 
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    Abstract
    Kann gut zitieren, aber schlecht rechnen: Künstliche Intelligenz besteht Juraprüfung an US-Uni
    Date
    27. 1.2023 16:22:55
  18. Plomin, R.; DeFries, J.C.: Erblichkeit kognitiver Stärken und Schwächen (1999) 0.07 
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    Abstract
    Schätzungsweise 30.000 unserer Gene werden im Gehirn ausgeprägt, und jedes könnte mit Intelligenz zu tun haben. Herauszufinden, welche davon bestimmt Komponenten unseres Intellekts mitgestalten oder zur Intelligenz allgemein beitragen, gleicht einer Sisyphusarbeit. Sie trägt aber erste Früchte
    Date
    22. 7.2000 19:01:54
  19. Zilm, G.: "Kl ist ein glorifizierter Taschenrechner" (2023) 0.07 
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    Abstract
    Künstliche Intelligenz zieht immer mehr in unseren Alltag ein. Dies macht vielen Angst. Sie fürchten, der Mensch könne die Kontrolle über Kl verlieren. Wie das verhindert werden kann, darüber macht sich das Ethik-Team am Deutschen Forschungszentrum für Künstliche Intelligenz Gedanken. Das DFKI hat mehrere Standorte, die Zentrale ist in Kaiserslautern.
    Date
    27. 1.2023 16:22:55
  20. Sokol, J.: Spielend lernen (2018) 0.07 
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    Abstract
    Die neuesten Versionen künstlicher Intelligenz wachsen ohne einprogrammiertes Vorwissen innerhalb weniger Stunden zu Weltklassespielern heran. Nun versuchen Forscher diese Systeme über das Spielbrett hinaus auf Probleme in Alltag und Wissenschaft anzuwenden - was nach wie vor knifflig ist. Darstellung der Strategien von AlphaGo und anderen Spielen zu Diskussion der Möglichkeiten und Grenzen der KI.
    Content
    "Maschinen mit menschlichen Fertigkeiten sind jedoch bisher nicht in Sicht. Selbst ein autodidaktischer Brettspielmeister wie AlphaZero hat in dieser Hinsicht noch einen weiten Weg vor sich. »Es gibt eine wirklich große Kluft zwischen dem wirklichen Denken, der kreativen Erforschung von Ideen und dem, was wir derzeit unter künstlicher Intelligenz verstehen«, sagt Josh Tenenbaum, ein Kognitionswissenschaftler am Massachusetts Institute of Technology. »Diese Art von Intelligenz gibt es zwar, aber sie existiert vor allem in den Köpfen großer KI-Forscher.«"

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