Search (93 results, page 3 of 5)

  • × language_ss:"e"
  • × theme_ss:"Retrievalalgorithmen"
  1. O'Leary, M.: DIALOG TARGET's new age searching (1993) 0.01 
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  2. Ojala, M.: Decisions, decisions, RANK decisions (1994) 0.01 
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  3. Hofferer, M.: Heuristic search in information retrieval (1994) 0.01 
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  4. Smith, M.; Smith, M.P.; Wade, S.J.: Applying genetic programming to the problem of term weight algorithms (1995) 0.01 
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  5. Robertson, M.; Willett, P.: ¬An upperbound to the performance of ranked output searching : optimal weighting of query terms using a genetic algorithms (1996) 0.01 
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  6. Ojala, M.: Commands that RANKle (1997) 0.01 
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  7. Vechtomova, O.; Karamuftuoglu, M.: Lexical cohesion and term proximity in document ranking (2008) 0.01 
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  8. Lalmas, M.: XML retrieval (2009) 0.01 
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    Type
    m
  9. 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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  10. Keen, M.: Query reformulation in ranked output interaction (1994) 0.01 
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  11. 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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  12. Lalmas, M.; Ruthven, I.: Representing and retrieving structured documents using the Dempster-Shafer theory of evidence : modelling and evaluation (1998) 0.01 
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  13. Brenner, E.H.: Beyond Boolean : new approaches in information retrieval; the quest for intuitive online search systems past, present & future (1995) 0.01 
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  14. Hancock-Beaulieu, M.; Walker, S.: ¬An evaluation of automatic query expansion in an online library catalogue (1992) 0.01 
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  15. Beaulieu, M.; Jones, S.: Interactive searching and interface issues in the Okapi best match probabilistic retrieval system (1998) 0.01 
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  16. Boughanem, M.; Chrisment, C.; Tamine, L.: On using genetic algorithms for multimodal relevance optimization in information retrieval (2002) 0.01 
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  17. 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.
  18. Vechtomova, O.; Karamuftuoglu, M.: Elicitation and use of relevance feedback information (2006) 0.01 
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  19. Lalmas, M.: XML information retrieval (2009) 0.01 
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  20. García Cumbreras, M.A.; Perea-Ortega, J.M.; García Vega, M.; Ureña López, L.A.: Information retrieval with geographical references : relevant documents filtering vs. query expansion (2009) 0.01 
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