Search (92 results, page 2 of 5)

  • × theme_ss:"Retrievalalgorithmen"
  1. Daniowicz, C.; Baliski, J.: Document ranking based upon Markov chains (2001) 0.01 
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  2. 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) 0.01 
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  3. Losee, R.M.; Church Jr., L.: Are two document clusters better than one? : the cluster performance question for information retrieval (2005) 0.01 
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    Abstract
    When do information retrieval systems using two document clusters provide better retrieval performance than systems using no clustering? We answer this question for one set of assumptions and suggest how this may be studied with other assumptions. The "Cluster Hypothesis" asks an empirical question about the relationships between documents and user-supplied relevance judgments, while the "Cluster Performance Question" proposed here focuses an the when and why of information retrieval or digital library performance for clustered and unclustered text databases. This may be generalized to study the relative performance of m versus n clusters.
  4. Bidoki, A.M.Z.; Yazdani, N.: an intelligent ranking algorithm for web pages : DistanceRank (2008) 0.01 
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  5. Savoy, J.; Ndarugendamwo, M.; Vrajitoru, D.: Report on the TREC-4 experiment : combining probabilistic and vector-space schemes (1996) 0.01 
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  6. Belkin, N.J.; Cool, C.; Koenemann, J.; Ng, K.B.; Park, S.: Using relevance feedback and ranking in interactive searching (1996) 0.01 
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  7. Beitzel, S.M.; Jensen, E.C.; Chowdhury, A.; Grossman, D.; Frieder, O; Goharian, N.: Fusion of effective retrieval strategies in the same information retrieval system (2004) 0.01 
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  8. Bar-Ilan, J.; Levene, M.: ¬The hw-rank : an h-index variant for ranking web pages (2015) 0.01 
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  9. Na, S.-H.; Kang, I.-S.; Roh, J.-E.; Lee, J.-H.: ¬An empirical study of query expansion and cluster-based retrieval in language modeling approach (2007) 0.01 
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  10. 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
  11. Faloutsos, C.: Signature files (1992) 0.01 
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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) 0.01 
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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) 0.01 
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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) 0.01 
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    Date
    13. 9.2014 14:45:22
  15. Fuhr, N.: Theorie des Information Retrieval I : Modelle (2004) 0.01 
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  16. Schaefer, A.; Jordan, M.; Klas, C.-P.; Fuhr, N.: Active support for query formulation in virtual digital libraries : a case study with DAFFODIL (2005) 0.01 
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  17. Fuhr, N.: Modelle im Information Retrieval (2023) 0.01 
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  18. Lee, J.-T.; Seo, J.; Jeon, J.; Rim, H.-C.: Sentence-based relevance flow analysis for high accuracy retrieval (2011) 0.01 
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  19. Jiang, J.-D.; Jiang, J.-Y.; Cheng, P.-J.: Cocluster hypothesis and ranking consistency for relevance ranking in web search (2019) 0.01 
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  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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