Search (75 results, page 1 of 4)

  • × theme_ss:"Retrievalalgorithmen"
  1. Zhang, W.; Korf, R.E.: Performance of linear-space search algorithms (1995) 0.05 
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    Date
    2. 8.1996 10:29:15
  2. Dominich, S.: Mathematical foundations of information retrieval (2001) 0.05 
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    Classification
    TVA (W)
    TLQ (W)
    Date
    22. 3.2008 12:26:32
    GHBS
    TVA (W)
    TLQ (W)
  3. Fan, W.; Fox, E.A.; Pathak, P.; Wu, H.: ¬The effects of fitness functions an genetic programming-based ranking discovery for Web search (2004) 0.03 
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    Date
    31. 5.2004 19:22:06
  4. Soulier, L.; Jabeur, L.B.; Tamine, L.; Bahsoun, W.: On ranking relevant entities in heterogeneous networks using a language-based model (2013) 0.03 
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    Date
    22. 3.2013 19:34:49
  5. Jacso, P.: Testing the calculation of a realistic h-index in Google Scholar, Scopus, and Web of Science for F. W. Lancaster (2008) 0.02 
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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. Fuhr, N.: Modelle im Information Retrieval (2013) 0.02 
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    Source
    Grundlagen der praktischen Information und Dokumentation. Handbuch zur Einführung in die Informationswissenschaft und -praxis. 6., völlig neu gefaßte Ausgabe. Hrsg. von R. Kuhlen, W. Semar u. D. Strauch. Begründet von Klaus Laisiepen, Ernst Lutterbeck, Karl-Heinrich Meyer-Uhlenried
  7. Khoo, C.S.G.; Wan, K.-W.: ¬A simple relevancy-ranking strategy for an interface to Boolean OPACs (2004) 0.02 
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    Source
    Electronic library. 22(2004) no.2, S.112-120
  8. Harman, D.; Fox, E.; Baeza-Yates, R.; Lee, W.: Inverted files (1992) 0.02 
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  9. Goffman, W.: ¬A searching procedure for information retrieval (1964) 0.02 
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  10. Liddy, E.D.; Paik, W.; McKenna, M.; Yu, E.S.: ¬A natural language text retrieval system with relevance feedback (1995) 0.01 
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  11. Ning, X.; Jin, H.; Jia, W.; Yuan, P.: Practical and effective IR-style keyword search over semantic web (2009) 0.01 
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  12. Fox, E.; Betrabet, S.; Koushik, M.; Lee, W.: Extended Boolean models (1992) 0.01 
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  13. Radev, D.; Fan, W.; Qu, H.; Wu, H.; Grewal, A.: Probabilistic question answering on the Web (2005) 0.01 
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  14. Zhang, W.; Yoshida, T.; Tang, X.: ¬A comparative study of TF*IDF, LSI and multi-words for text classification (2011) 0.01 
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  15. Wei, F.; Li, W.; Lu, Q.; He, Y.: Applying two-level reinforcement ranking in query-oriented multidocument summarization (2009) 0.01 
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  16. Wei, F.; Li, W.; Liu, S.: iRANK: a rank-learn-combine framework for unsupervised ensemble ranking (2010) 0.01 
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  17. Lee, J.; Min, J.-K.; Oh, A.; Chung, C.-W.: Effective ranking and search techniques for Web resources considering semantic relationships (2014) 0.01 
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  18. Liu, X.; Zheng, W.; Fang, H.: ¬An exploration of ranking models and feedback method for related entity finding (2013) 0.01 
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  19. Wiggers, G.; Verberne, S.; Loon, W. van; Zwenne, G.-J.: Bibliometric-enhanced legal information retrieval : combining usage and citations as flavors of impact relevance (2023) 0.01 
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  20. 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

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  • d 12
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  • el 1
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