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Adaptive Information Filtering Algorithms

  • Daniel R. Tauritz
  • Ida G. Sprinkhuizen-Kuyper
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1642)

Abstract

Adaptive Information Filtering is concerned with filtering information streams in changing environments. The changes may occur both on the transmission side (the nature of the streams can change) and on the reception side (the interests of a user can change). The research described in this paper details the progress made in a prototype Adaptive Information Filtering system based on weighted trigram analysis and evolutionary computation. The main improvements of the algorithms employed by the system concern the computation of the distance between weighted trigram vectors and a further analysis of the two-pool evolutionary algorithm. We tested our new prototype system on the Reuters-21578 text categorization test collection.

Keywords

Weight Vector Object Space Earning Forecast Trial Solution Document Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Boyce, Bert R., Meadow, Charles T., Kraft, Donald H. (1994). Measurement in Information Science, Academic Press.Google Scholar
  2. 2.
    Cavnar, William B. (1994) “Using An N-Gram-Based Document Representation With A Vector Processing Retrieval Model” in “Overview of the Third Text REtrieval Conference (TREC-3)”, D. K. Harman (ed.), National Institute of Standards and Technology (NIST) Special Publications 500–225, April 1995.Google Scholar
  3. 3.
    De Heer, T. (1982). “The Application of the Concept of Homeosemy to Natural Language Information Retrieval”, Information Processing and Management 18, No.5, pp.229–236.CrossRefGoogle Scholar
  4. 4.
    Jones, Karen Sparck, Willett, Peter (eds.) (1997). Readings in Information Retrieval, Morgan Kaufman, July 1997.Google Scholar
  5. 5.
    Michalewicz, Zbigniew (1996). Genetic Algorithms + Data Structures = Evolution Programs, 3rd revised and extended edition, Springer-Verlag.Google Scholar
  6. 6.
    Rijsbergen, C. J., van (1979). Information Retrieval, 2nd edition, Butterworths, London.Google Scholar
  7. 7.
    Schmidt, S., and Teufel, B. (1988). “Full text retrieval based on syntactic similarities”, Information Systems, Vol. 13, No. 1, pp. 65–70.zbMATHCrossRefGoogle Scholar
  8. 8.
    Sheth, Beered Dilip (1994). “A Learning Approach to Personalized Information Filtering”, M.Sc. thesis, Massachusetts Institute of Technology, U.S.A.Google Scholar
  9. 9.
    Tauritz, Daniel R. (1996). Adaptive Information Filtering as a means to overcome Information Overload, M.Sc. thesis, Internal Report 96-35, Department of Computer Science, Leiden University, The Netherlands. Available via: http://www.wi.leidenuniv.nl/MScThesis/IR96-35.html Google Scholar
  10. 10.
    Tauritz, Daniel R., Kok, Joost N., Sprinkhuizen-Kuyper, Ida G. (1997). “Adaptive Information Filtering using Evolutionary Computation”, Joint Conference of Information Sciences 1997, Vol.1, pp.77–80.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Daniel R. Tauritz
    • 1
  • Ida G. Sprinkhuizen-Kuyper
    • 1
  1. 1.LIACSLeiden UniversityLeiden

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