KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Applied Mathematics and Computational Science Program
Extensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
Permanent link to this recordhttp://hdl.handle.net/10754/563101
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AbstractBased on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification-exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).
SponsorsThis research was supported by King Abdullah University of Science and Technology in the frameworks of joint project with Nizhni Novgorod State University "Novel Algorithms in Machine Learning and Computer Vision, and Their High Performance Implementations", Russian Federal Program "Research and Development in Prioritized Directions of Scientific-Technological Complex of Russia in 2007-2013". The authors wish to express their gratitude to anonymous reviewers for useful comments.