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.
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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).
Wallin E.: Optimized Sequence Matching on the CM-2; Masters Thesis, Royal Inst. of Technology, Sweden (1992).
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)
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© 1993 Springer-Verlag London Limited
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Levin, B., Lansner, A. (1993). Document Retrieval and Protein Sequence Matching using a Neural Network. In: Gielen, S., Kappen, B. (eds) ICANN ’93. ICANN 1993. Springer, London. https://doi.org/10.1007/978-1-4471-2063-6_137
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DOI: https://doi.org/10.1007/978-1-4471-2063-6_137
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