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Author Mephu-Nguifo, E.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©1994
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Lattices ♦ Learning systems ♦ Law ♦ Legal factors ♦ Neural networks ♦ Sequences ♦ Voting ♦ DNA ♦ Computer architecture ♦ Machine learning
Abstract The previously-reported LEGAL system is an empirical machine learning system based on Galois Lattice. Its aim is first to produce a semi-lattice from a concept denoted by a set of objects which are described with binary attributes. Then using some selected attribute conjunctions in the semi-lattice and a majority vote principle, LEGAL predicts new examples from unseen objects. This paper describes a new version LEGAL-E and its application to two biological problems: the prediction of splice junctions sites and the promoter recognition. Results obtained are far better than those of some symbolic learning systems, and are as better as those of some best neural networks methods. Moreover some empirical properties shared by LEGAL-E and neural networks are described. Finally this paper shows how the semi-lattice can be used as a dynamic neural network architecture in order to combine both learning techniques for knowledge refinement.
Description Author affiliation: LIRMM, Univ. des Sci. et Tech. du Languedoc, Montpellier, France (Mephu-Nguifo, E.)
ISBN 0818667850
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1994-11-06
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 746.08 kB
Page Count 7
Starting Page 461
Ending Page 467


Source: IEEE Xplore Digital Library