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  • × author_ss:"Robertson, A.M."
  • × author_ss:"Willett, P."
  1. Robertson, A.M.; Willett, P.: Use of genetic algorithms in information retrieval (1995) 0.00 
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    Abstract
    Reviews the basic techniques involving genetic algorithms and their application to 2 problems in information retrieval: the generation of equifrequent groups of index terms; and the identification of optimal query and term weights. The algorithm developed for the generation of equifrequent groupings proved to be effective in operation, achieving results comparable with those obtained using a good deterministic algorithm. The algorithm developed for the identification of optimal query and term weighting involves fitness function that is based on full relevance information
  2. Robertson, A.M.; Willett, P.: Applications of n-grams in textual information systems (1998) 0.00 
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    Abstract
    Provides an introduction to the use of n-grams in textual information systems, where an n-gram is a string of n, usually adjacent, characters, extracted from a section of continuous text. Applications that can be implemented efficiently and effectively using sets of n-grams include spelling errors detection and correction, query expansion, information retrieval with serial, inverted and signature files, dictionary look up, text compression, and language identification
  3. Robertson, A.M.; Willett, P.: Retrieval techniques for historical English text : searching the sixteenth and seventeenth century titles in the Catalogue of Caterbury Cathedral Library using spelling-correction methods (1992) 0.00 
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    Imprint
    Oxford : Learned Information Ltd.
    Source
    Online information 92. Proc. of the 16th Int. Online Information Meeting, London, 8-10.12.1992. Ed. by David I. Raitt
  4. Jones, G.; Robertson, A.M.; Willett, P.: ¬An introduction to genetic algorithms and to their use in information retrieval (1994) 0.00 
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    Abstract
    This paper provides an introduction to genetic algorithms, a new approach to the investigation of computationally-intensive problems that may be insoluble using conventional, deterministic approaches. A genetic algorithm takes an initial set of possible starting solutions and then iteratively improves theses solutions using operators that are analogous to those involved in Darwinian evolution. The approach is illusrated by reference to several problems in information retrieval
  5. Ekmekcioglu, F.C.; Robertson, A.M.; Willett, P.: Effectiveness of query expansion in ranked-output document retrieval systems (1992) 0.00 
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    Source
    Journal of information science. 18(1992) no.2, S.139-147

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