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ICANN ’93 pp 511-511 | Cite as

Document Retrieval and Protein Sequence Matching using a Neural Network

  • Björn Levin
  • Anders Lansner
Conference paper

Abstract

During the development of algorithms for the generation of higher order complex units in neural networks we have come to be interested in free text document retrieval and protein sequence matching. Document retrieval, as it is percieved here, consists of returning a list of the documents in the library that are the most relevant according to a description of a subject, sorted according to descending probability of relevance. Obviously, the key is having a reasonable measure of similarity between the individual documents and a given concept, a measure here provided by the neural network. [1] contains an overview of similar work.

Keywords

Neural Network Editing Distance Ranking List Sensor Unit Document Retrieval 
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.

References

  1. [1]
    Doszkocs T., Reggia J., and Lin X.: Connectionist Models and Information Retrieval; Annual Review of Information Science and Technology (ARIST), Vol. 25 pp 209–260 (1900).Google Scholar
  2. [2]
    Wallin E.: Optimized Sequence Matching on the CM-2; Masters Thesis, Royal Inst. of Technology, Sweden (1992).Google Scholar
  3. [3]
    Levin, B. & Lansner, A.: Document Retrieval, Protein Sequence Matching and Sensor Selection Methods using a Neural Network; Royal Inst. of Technology, Tech. rep. TRITA-NA-P9238 (1992)Google Scholar

Copyright information

© Springer-Verlag London Limited 1993

Authors and Affiliations

  • Björn Levin
    • 1
  • Anders Lansner
    • 1
  1. 1.SANS, NADARoyal Institute of TechnologyStockholmSweden

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