Search (124 results, page 2 of 7)

  • × theme_ss:"Retrievalalgorithmen"
  1. Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986) 0.00 
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    Date
    14. 6.2015 22:12:56
  2. Lopez-Pujalte, C.; Guerrero Bote, V.P.; Moya-Anegón, F. de: Evaluation of the application of genetic algorithms to relevance feedback (2003) 0.00 
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    Source
    Challenges in knowledge representation and organization for the 21st century: Integration of knowledge across boundaries. Proceedings of the 7th ISKO International Conference Granada, Spain, July 10-13, 2002. Ed.: M. López-Huertas
  3. Karisani, P.; Rahgozar, M.; Oroumchian, F.: Transforming LSA space dimensions into a rubric for an automatic assessment and feedback system (2016) 0.00 
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  4. Elsweiler, D.; Kruschwitz, U.: Interaktives Information Retrieval (2023) 0.00 
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    Imprint
    Berlin : DeGruyter
  5. Jacso, P.: Testing the calculation of a realistic h-index in Google Scholar, Scopus, and Web of Science for F. W. Lancaster (2008) 0.00 
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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.
    Content
    Beitrag in einem Themenheft 'The Influence of F. W. Lancaster on Information Science and on Libraries', das als Festschrift für F.W. Lancaster deklariert ist.
  6. Reimer, U.: Empfehlungssysteme (2023) 0.00 
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    Imprint
    Berlin : DeGruyter
  7. 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
  8. Faloutsos, C.: Signature files (1992) 0.00 
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    Date
    7. 5.1999 15:22:48
  9. 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
  10. 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
  11. 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
  12. 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
  13. Kanaeva, Z.: Ranking: Google und CiteSeer (2005) 0.00 
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    Date
    20. 3.2005 16:23:22
  14. Can, F.: Incremental clustering for dynamic information processing (1993) 0.00 
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  15. Wartik, S.; Fox, E.; Heath, L.; Chen, Q.-F.: Hashing algorithms (1992) 0.00 
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  16. Guerrero-Bote, V.P.; Moya Anegón, F. de; Herrero Solana, V.: Document organization using Kohonen's algorithm (2002) 0.00 
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  17. Fuhr, N.: Modelle im Information Retrieval (2023) 0.00 
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    Imprint
    Berlin : DeGruyter
  18. López-Pujalte, C.; Guerrero-Bote, V.P.; Moya-Anegón, F. de: Genetic algorithms in relevance feedback : a second test and new contributions (2003) 0.00 
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  19. López-Herrera, A.G.; Herrera-Viedma, E.; Herrera, F.: ¬A study of the use of multi-objective evolutionary algorithms to learn Boolean queries : a comparative study (2009) 0.00 
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  20. Joss, M.W.; Wszola, S.: ¬The engines that can : text search and retrieval software, their strategies, and vendors (1996) 0.00 
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    Date
    12. 9.1996 13:56:22

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