Iterative adaptation algorithms in multicriteria tasks

This article examines using of iterative adaptation algorithms to solve the problem of determining measurement location of the carotid artery intima-media complex. The formulation of a multi-criteria decision-making problem, as the basis for determining correct criterion for proper selection and successful recognizing of the required object in an ultrasound image. The work discusses principles of constructing cascade classi ers as well as, the use of the cascade Haar classi er and the cascade LBP classi er, for which Haar primitives and local binary templates are used as a basis. The results of experimental studies in order to determine e ectivity of di erent boosting algorithms to solve this problem are presented. The best results were shown by the Haar cascade classi er, developed using an iterative adaptation algorithm, which manages solving a multicriteria problem on a given training set more successfully and determines the most suitable areas for measuring the thickness of the common carotid artery intimate media complex.