Methods of recognition based on the function of rival similarity

A unified approach to the development of pattern recognition methods is proposed. The approach is based on the employment of the function of rival similarity (FRiS-function), which adequately represents human methods of evaluation of similarity and difference. Methods of recognition based on this approach are briefly described. Examples of solution of actual and benchmark problems using FRiS-function are given. Nikolai Grigor’evich Zagoruiko finished a secondary school in Novosibirsk oblast. Graduated from the Electroengineering Department of the Leningrad Institute of Motion-picture Engineers in 1953. Since 1960 has been working at the Institute of Mathematics of the Siberian Branch of the Russian Academy of Sciences. Received candidate’s degree in 1962 and doctoral degree in Pattern Recognition in 1969. Since 1973 to 1982 had been working as a vice-rector of the Novosibirsk State University. In 1988–1990 had been heading a project in the International Laboratory of Artificial Intelligence in Bratislava (Slovakia). Scientific interests: pattern recognition and prediction. Author of 219 publications, including 13 monographs. Vladimir Vladimirovich Dyubanov. Born 1981. Graduated from the Krasnoyarsk State University in 2002. Currently is a postgraduate at the Novosibirsk State University. Scientific interests: data mining and pattern recognition and prognosis. Author of eight papers. Ol’ga Andreevna Kutnenko. Born 1958. Graduated from the Novosibirsk State University in 1980. Received candidate’s degree in 2000. Scientific interests: data analysis, determination of empirical principles, pattern recognition. Author of 13 papers. Irina Artemovna Borisova. Born 1978. Graduated from the Novosibirsk State University in 2002. Currently is a chief programmer at the Institute of Mathematics, Siberian Division, Russian Academy of Sciences. Scientific interests: pattern recognition. Author of nine papers.