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by James A. Inglehart, Michaelt Cornelison, Andpeter C. Nelson
http://www.ai.eecs.uic.edu/~mcornel/Papers/RM.ps.gz
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Abstract:
Abstract---The main contribution of this report is the suggestion, development, and trial of a new technique for finding plausible solutions to the class of simple linear systems which can be represented by difference constraints, in cases where the differences are the observed values of random variables, instead of known constants. It can find solutions when measurement errors exceed pre-selected fixed bounds, as is often the case for DNA restriction mapping problems. The technique is based upon conventional generate-and-test methods, but utilizes a trained neural net instead of binary logic to prune implausible partially-constructed solutions. It is applied to certain restriction mapping problems, and a method of net training is discovered which almost eliminates false negatives, an imperative in this particular problem domain. Keywords---Simple linear systems, Difference constraints, DNA Restriction mapping.
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