Search (40 results, page 1 of 2)

  • × theme_ss:"Retrievalalgorithmen"
  1. Couvreur, T.R.; Benzel, R.N.; Miller, S.F.; Zeitler, D.N.; Lee, D.L.; Singhal, M.; Shivaratri, N.; Wong, W.Y.P.: ¬An analysis of performance and cost factors in searching large text databases using parallel search systems (1994) 0.06 
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  2. Lee, D.L.; Ren, L.: Document ranking on weight-partitioned signature files (1996) 0.06 
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  3. Dang, E.K.F.; Luk, R.W.P.; Allan, J.; Ho, K.S.; Chung, K.F.L.; Lee, D.L.: ¬A new context-dependent term weight computed by boost and discount using relevance information (2010) 0.04 
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  4. Archuby, C.G.: Interfaces se recuperacion para catalogos en linea con salidas ordenadas por probable relevancia (2000) 0.02 
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    Source
    Ciencia da informacao. 29(2000) no.3, S.5-13
  5. Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.02 
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    Source
    Information processing and management. 22(1986) no.6, S.465-476
  6. Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983) 0.01 
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    Date
    30. 3.2001 13:32:22
  7. Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000) 0.01 
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    Date
    25. 8.2005 17:42:22
  8. Costa Carvalho, A. da; Rossi, C.; Moura, E.S. de; Silva, A.S. da; Fernandes, D.: LePrEF: Learn to precompute evidence fusion for efficient query evaluation (2012) 0.01 
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  9. Reimer, U.: Empfehlungssysteme (2023) 0.01 
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    Abstract
    Mit der wachsenden Informationsflut steigen die Anforderungen an Informationssysteme, aus der Menge potenziell relevanter Information die in einem bestimmten Kontext relevanteste zu selektieren. Empfehlungssysteme spielen hier eine besondere Rolle, da sie personalisiert - d. h. kontextspezifisch und benutzerindividuell - relevante Information herausfiltern können. Definition: Ein Empfehlungssystem empfiehlt einem Benutzer bzw. einer Benutzerin in einem definierten Kontext aus einer gegebenen Menge von Empfehlungsobjekten eine Teilmenge als relevant. Empfehlungssysteme machen Benutzer auf Objekte aufmerksam, die sie möglicherweise nie gefunden hätten, weil sie nicht danach gesucht hätten oder sie in der schieren Menge an insgesamt relevanter Information untergegangen wären.
  10. Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.01 
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    Date
    14. 6.2015 22:12:44
  11. Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986) 0.01 
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    Date
    14. 6.2015 22:12:56
  12. Wilhelmy, A.: Phonetische Ähnlichkeitssuche in Datenbanken (1991) 0.01 
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    Abstract
    In dialoggesteuerten Systemen zur Informationswiedergewinnung (Information Retrieval Systems, IRS) kann man - vergröbernd - das Wechselspiel zwischen Mensch und Computer als iterativen Prozess zur Erhöhung von Genauigkeit (Precision) auf der einen und Vollständigkeit (Recall) der Nachweise auf der anderen Seite verstehen. Vorgestellt wird ein maschinell anwendbares Verfahren, das auf phonologische Untersuchungen des Sprachwissenschaftlers Nikolaj S. Trubetzkoy (1890-1938) zurückgeht. In den Grundzügen kann es erheblich zur Verbesserung der Nachweisvollständigkeit beitragen. Dadurch, daß es die 'Ähnlichkeitsumgebungen' von Suchbegriffen in die Recherche mit einbezieht, zeigt es sich vor allem für Systeme mit koordinativer maschineller Indexierung als vorteilhaft. Bei alphabetischen Begriffen erweist sich die Einführung eines solchen zunächst nur auf den Benutzer hin orientierten Verfahrens auch aus technischer Sicht als günstig, da damit die Anzahl der Zugriffe bei den Suchvorgängen auch für große Datenvolumina niedrig gehalten werden kann
  13. Moura, E.S. de; Fernandes, D.; Ribeiro-Neto, B.; Silva, A.S. da; Gonçalves, M.A.: Using structural information to improve search in Web collections (2010) 0.01 
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  14. Lanvent, A.: Praxis - Windows-Suche und Indexdienst : Auch Windows kann bei der Suche den Turbo einlegen: mit dem Indexdienst (2004) 0.01 
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    Content
    "Für eine 4-GByte-Festplatte mit mehreren Partitionen sucht Windows XP im Volltextmodus weit über zwei Stunden. Der Indexdienst verkürzt diese Recherchedauer drastisch um mehr als eine Stunde. Im Gegensatz zu den Indizes der kommerziellen Suchwerkzeuge erfasst der Windows-Indexdienst nur Text-, HTML- und OfficeDateien über entsprechend integrierte Dokumentfilter. Da er weder ZIP-Files noch PDFs erkennt und auch keine E-Mails scannt, ist er mit komplexen Anfragen schnell überfordert. Standardmäßig ist der Indexdienst zwar installiert, aber nicht aktiviert. Das erledigt der Anwender über Start/Arbeitsplatz und den Befehl Verwalten aus dem Kontextmenü. In der Computerverwaltung aktiviert der Benutzer den Eintrag Indexdienst und wählt Starten aus dem Kontextmenü. Die zu indizierenden Elemente verwaltet Windows über so genannte Kataloge, mit deren Hilfe der User bestimmt, welche Dateitypen aus welchen Ordnern indiziert werden sollen. Zwar kann der Anwender neben dem Katalog System weitere Kataloge einrichten. Ausreichend ist es aber in den meisten Fällen, dem Katalog System weitere Indizierungsordner über die Befehle Neu/Verzeichnis hinzuzufügen. Klickt der Benutzer dann einen der Indizierungsordner mit der rechten Maustaste an und wählt Alle Tasks/Erneut prüfen (Vollständig), beginnt der mitunter langwierige Indizierungsprozess. Über den Eigenschaften-Dialog lässt sich allerdings der Leistungsverbrauch drosseln. Eine inkrementelle Indizierung, bei der Windows nur neue Elemente im jeweiligen Verzeichnis unter die Lupe nimmt, erreicht der Nutzer über Alle Tasks/Erneut prüfen (inkrementell). Einschalten lässt sich der Indexdienst auch über die Eigenschaften eines Ordners und den Befehl Erweitert/ln-halt für schnelle Dateisuche indizieren. Auskunft über die dem Indexdienst zugeordneten Ordner und Laufwerke erhalten Sie, wenn Sie die WindowsSuche starten und Weitere Optionen/ Andere Suchoptionen/Bevorzugte Einstellungen ändern/Indexdienst verwenden anklicken."
  15. MacFarlane, A.; Robertson, S.E.; McCann, J.A.: Parallel computing for passage retrieval (2004) 0.01 
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    Date
    20. 1.2007 18:30:22
  16. Faloutsos, C.: Signature files (1992) 0.01 
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    Date
    7. 5.1999 15:22:48
  17. Losada, D.E.; Barreiro, A.: Emebedding term similarity and inverse document frequency into a logical model of information retrieval (2003) 0.01 
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    Date
    22. 3.2003 19:27:23
  18. Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014) 0.01 
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    Date
    22. 8.2014 17:05:18
  19. Tober, M.; Hennig, L.; Furch, D.: SEO Ranking-Faktoren und Rang-Korrelationen 2014 : Google Deutschland (2014) 0.01 
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    Date
    13. 9.2014 14:45:22
  20. Ravana, S.D.; Rajagopal, P.; Balakrishnan, V.: Ranking retrieval systems using pseudo relevance judgments (2015) 0.01 
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    Date
    20. 1.2015 18:30:22
    18. 9.2018 18:22:56

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