Search (185 results, page 5 of 10)

  • × theme_ss:"Retrievalalgorithmen"
  1. Moffat, A.; Bell, T.A.H.: In situ generation of compressed inverted files (1995) 0.01 
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    Source
    Journal of the American Society for Information Science. 46(1995) no.7, S.537-550
  2. Kelledy, F.; Smeaton, A.F.: Signature files and beyond (1996) 0.01 
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    Abstract
    Proposes that signature files be used as a viable alternative to other indexing strategies such as inverted files for searching through large volumes of text. Demonstrates through simulation, that search times can be further reduced by enhancing the basic signature file concept using deterministic partitioning algorithms which eliminate the need for an exhaustive search of the entire signature file. Reports research to evaluate the performance of some deterministic partitioning algorithms in a non simulated environment using 276 MB of raw newspaper text (taken from the Wall Street Journal) and real user queries. Presents a selection of results to illustrate trends and highlight important aspects of the performance of these methods under realistic rather than simulated operating conditions. As a result of the research reported here certain aspects of this approach to signature files are shown to be found wanting and require improvement. Suggests lines of future research on the partitioning of signature files
  3. Savoy, J.: Ranking schemes in hybrid Boolean systems : a new approach (1997) 0.01 
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    Journal of the American Society for Information Science. 48(1997) no.3, S.235-253
  4. Martin-Bautista, M.J.; Vila, M.-A.; Larsen, H.L.: ¬A fuzzy genetic algorithm approach to an adaptive information retrieval agent (1999) 0.01 
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    Journal of the American Society for Information Science. 50(1999) no.9, S.760-771
  5. Baeza-Yates, R.; Navarro, G.: Block addressing indices for approximate text retrieval (2000) 0.01 
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    Journal of the American Society for Information Science. 51(2000) no.1, S.69-82
  6. Zhu, B.; Chen, H.: Validating a geographical image retrieval system (2000) 0.01 
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    Journal of the American Society for Information Science. 51(2000) no.7, S.625-634
  7. French, J.C.; Powell, A.L.; Schulman, E.: Using clustering strategies for creating authority files (2000) 0.01 
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    Journal of the American Society for Information Science. 51(2000) no.8, S.774-786
  8. Aigrain, P.; Longueville, V.: ¬A model for the evaluation of expansion techniques in information retrieval systems (1994) 0.01 
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    Journal of the American Society for Information Science. 45(1994) no.4, S.225-234
  9. Kwok, K.L.: ¬A network approach to probabilistic information retrieval (1995) 0.01 
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    Abstract
    Shows how probabilistic information retrieval based on document components may be implemented as a feedforward (feedbackward) artificial neural network. The network supports adaptation of connection weights as well as the growing of new edges between queries and terms based on user relevance feedback data for training, and it reflects query modification and expansion in information retrieval. A learning rule is applied that can also be viewed as supporting sequential learning using a harmonic sequence learning rate. Experimental results with 4 standard small collections and a large Wall Street Journal collection show that small query expansion levels of about 30 terms can achieve most of the gains at the low-recall high-precision region, while larger expansion levels continue to provide gains at the high-recall low-precision region of a precision recall curve
  10. Chen, H.; Zhang, Y.; Houston, A.L.: Semantic indexing and searching using a Hopfield net (1998) 0.01 
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    Source
    Journal of information science. 24(1998) no.1, S.3-18.
  11. Cannane, A.; Williams, H.E.: General-purpose compression for efficient retrieval (2001) 0.01 
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    Journal of the American Society for Information Science and technology. 52(2001) no.5, S.430-437
  12. Bodoff, D.; Enache, D.; Kambil, A.; Simon, G.; Yukhimets, A.: ¬A unified maximum likelihood approach to document retrieval (2001) 0.01 
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    Journal of the American Society for Information Science and technology. 52(2001) no.10, S.785-796
  13. Nie, J.-Y.: Query expansion and query translation as logical inference (2003) 0.01 
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    Journal of the American Society for Information Science and technology. 54(2003) no.4, S.335-346
  14. 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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    Journal of the American Society for Information Science and technology. 54(2003) no.4, S.281-284
  15. Bodoff, D.; Robertson, S.: ¬A new unified probabilistic model (2004) 0.01 
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    Journal of the American Society for Information Science and Technology. 55(2004) no.6, S.471-487
  16. Fan, W.; Fox, E.A.; Pathak, P.; Wu, H.: ¬The effects of fitness functions an genetic programming-based ranking discovery for Web search (2004) 0.01 
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    Journal of the American Society for Information Science and technology. 55(2004) no.7, S.628-636
  17. 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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    Journal of the American Society for Information Science and Technology. 55(2004) no.10, S.859-868
  18. Dominich, S.; Skrop, A.: PageRank and interaction information retrieval (2005) 0.01 
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    Journal of the American Society for Information Science and Technology. 56(2005) no.1, S.63-69
  19. Radev, D.; Fan, W.; Qu, H.; Wu, H.; Grewal, A.: Probabilistic question answering on the Web (2005) 0.01 
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    Journal of the American Society for Information Science and Technology. 56(2005) no.6, S.571-583
  20. Thelwall, M.: Can Google's PageRank be used to find the most important academic Web pages? (2003) 0.01 
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    Journal of documentation. 59(2003) no.2, S.205-217

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