Abstract: The task of smooth and stable decision rules construction in logical recognition models is considered.
Logical regularities of classes are defined as conjunctions of one-place predicates that determine the
membership of features values in an intervals of the real axis. The conjunctions are true on a special no
extending subsets of reference objects of some class and are optimal. The standard approach of linear decision
rules construction for given sets of logical regularities consists in realization of voting schemes. The weighting
coefficients of voting procedures are done as heuristic ones or are as solutions of complex optimization task. The
modifications of linear decision rules are proposed that are based on the search of maximal estimations of
standard objects for their classes and use approximations of logical regularities by smooth sigmoid functions.
Keywords: precedent-recognition recognition, logical regularities of classes, estimate calculation algorithms,
integer programming, decision rules, sigmoid formatting rules
OPTIMAL DECISION RULES IN LOGICAL RECOGNITION MODELS
Anatol Gupal, Vladimir Ryazanov