Search (43 results, page 1 of 3)

  • × theme_ss:"Retrievalalgorithmen"
  1. Kelledy, F.; Smeaton, A.F.: Signature files and beyond (1996) 0.01
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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
  2. 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) 0.01
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    Date
    22. 3.2003 19:27:36
  3. Crestani, F.: Combination of similarity measures for effective spoken document retrieval (2003) 0.00
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  4. 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.
  5. Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.00
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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) 0.00
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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) 0.00
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    Date
    25. 8.2005 17:42:22
  8. Can, F.: Incremental clustering for dynamic information processing (1993) 0.00
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  9. Wartik, S.; Fox, E.; Heath, L.; Chen, Q.-F.: Hashing algorithms (1992) 0.00
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  10. Guerrero-Bote, V.P.; Moya Anegón, F. de; Herrero Solana, V.: Document organization using Kohonen's algorithm (2002) 0.00
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  11. 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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  12. 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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  13. Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.00
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    Date
    14. 6.2015 22:12:44
  14. Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986) 0.00
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    Date
    14. 6.2015 22:12:56
  15. 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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  16. López-Pujalte, C.; Guerrero-Bote, V.P.; Moya-Anegón, F. de: Order-based fitness functions for genetic algorithms applied to relevance feedback (2003) 0.00
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  17. Wei, F.; Li, W.; Lu, Q.; He, Y.: Applying two-level reinforcement ranking in query-oriented multidocument summarization (2009) 0.00
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  18. Wei, F.; Li, W.; Liu, S.: iRANK: a rank-learn-combine framework for unsupervised ensemble ranking (2010) 0.00
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  19. Tsai, C.-F.; Hu, Y.-H.; Chen, Z.-Y.: Factors affecting rocchio-based pseudorelevance feedback in image retrieval (2015) 0.00
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  20. 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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