Search (33 results, page 1 of 2)

  • × theme_ss:"Retrievalalgorithmen"
  1. Joss, M.W.; Wszola, S.: ¬The engines that can : text search and retrieval software, their strategies, and vendors (1996) 0.03 
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    Abstract
    Traces the development of text searching and retrieval software designed to cope with the increasing demands made by the storage and handling of large amounts of data, recorded on high data storage media, from CD-ROM to multi gigabyte storage media and online information services, with particular reference to the need to cope with graphics as well as conventional ASCII text. Includes details of: Boolean searching, fuzzy searching and matching; relevance ranking; proximity searching and improved strategies for dealing with text searching in very large databases. Concludes that the best searching tools for CD-ROM publishers are those optimized for searching and retrieval on CD-ROM. CD-ROM drives have relatively lower random seek times than hard discs and so the software most appropriate to the medium is that which can effectively arrange the indexes and text on the CD-ROM to avoid continuous random access searching. Lists and reviews a selection of software packages designed to achieve the sort of results required for rapid CD-ROM searching
    Date
    12. 9.1996 13:56:22
    Source
    CD-ROM professional. 9(1996) no.6, S.30+(14 S.)
  2. Jacso, P.: Testing the calculation of a realistic h-index in Google Scholar, Scopus, and Web of Science for F. W. Lancaster (2008) 0.01 
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    Abstract
    This paper focuses on the practical limitations in the content and software of the databases that are used to calculate the h-index for assessing the publishing productivity and impact of researchers. To celebrate F. W. Lancaster's biological age of seventy-five, and "scientific age" of forty-five, this paper discusses the related features of Google Scholar, Scopus, and Web of Science (WoS), and demonstrates in the latter how a much more realistic and fair h-index can be computed for F. W. Lancaster than the one produced automatically. Browsing and searching the cited reference index of the 1945-2007 edition of WoS, which in my estimate has over a hundred million "orphan references" that have no counterpart master records to be attached to, and "stray references" that cite papers which do have master records but cannot be identified by the matching algorithm because of errors of omission and commission in the references of the citing works, can bring up hundreds of additional cited references given to works of an accomplished author but are ignored in the automatic process of calculating the h-index. The partially manual process doubled the h-index value for F. W. Lancaster from 13 to 26, which is a much more realistic value for an information scientist and professor of his stature.
  3. Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.01 
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    Source
    Information processing and management. 22(1986) no.6, S.465-476
  4. Jones, K.: Linguistic searching versus relevance ranking : DR-LINK and TARGET (1999) 0.01 
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    Source
    Online and CD-ROM review. 23(1999) no.2, S.67-80
  5. 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
  6. 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
  7. Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.01 
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    Date
    14. 6.2015 22:12:44
  8. Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986) 0.01 
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    Date
    14. 6.2015 22:12:56
  9. Jones, G.; Robertson, A.M.; Willett, P.: ¬An introduction to genetic algorithms and to their use in information retrieval (1994) 0.00 
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    Source
    Online and CD-ROM review. 18(1994) no.1, S.3-12
  10. MacFarlane, A.; Robertson, S.E.; McCann, J.A.: Parallel computing for passage retrieval (2004) 0.00 
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    Date
    20. 1.2007 18:30:22
  11. Faloutsos, C.: Signature files (1992) 0.00 
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    Date
    7. 5.1999 15:22:48
  12. Losada, D.E.; Barreiro, A.: Emebedding term similarity and inverse document frequency into a logical model of information retrieval (2003) 0.00 
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    Date
    22. 3.2003 19:27:23
  13. Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014) 0.00 
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    Date
    22. 8.2014 17:05:18
  14. Tober, M.; Hennig, L.; Furch, D.: SEO Ranking-Faktoren und Rang-Korrelationen 2014 : Google Deutschland (2014) 0.00 
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    Date
    13. 9.2014 14:45:22
  15. Ravana, S.D.; Rajagopal, P.; Balakrishnan, V.: Ranking retrieval systems using pseudo relevance judgments (2015) 0.00 
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    Date
    20. 1.2015 18:30:22
    18. 9.2018 18:22:56
  16. Chang, C.-H.; Hsu, C.-C.: Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval (1998) 0.00 
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    Date
    1. 8.1996 22:08:06
  17. Kanaeva, Z.: Ranking: Google und CiteSeer (2005) 0.00 
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    Date
    20. 3.2005 16:23:22
  18. Nagelschmidt, M.: Verfahren zur Anfragemodifikation im Information Retrieval (2008) 0.00 
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    Pages
    V, 110 S. + CD
  19. Kelledy, F.; Smeaton, A.F.: Signature files and beyond (1996) 0.00 
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    Source
    Information retrieval: new systems and current research. Proceedings of the 16th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Drymen, Scotland, 22-23 Mar 94. Ed.: R. Leon
  20. Crestani, F.; Dominich, S.; Lalmas, M.; Rijsbergen, C.J.K. van: Mathematical, logical, and formal methods in information retrieval : an introduction to the special issue (2003) 0.00 
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    Date
    22. 3.2003 19:27:36

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