Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/1182

 Title: Optimal Decision Rules in Logical Recognition Models Authors: Gupal, AnatolRyazanov, Vladimir Keywords: Precedent-Recognition RecognitionLogical Regularities of ClassesEstimate Calculation AlgorithmsInteger ProgrammingDecision RulesSigmoid Formatting Rules Issue Date: 2009 Publisher: Institute of Information Theories and Applications FOI ITHEA 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. URI: http://hdl.handle.net/10525/1182 ISSN: 1313-0455 Appears in Collections: Book 08 Classification Forecasting Data Mining

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