Dynamic Programming Approach for Exact Decision Rule Optimization
Type
ArticleKAUST Department
Applied Mathematics and Computational Science ProgramComputer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Extensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
Date
2013Permanent link to this record
http://hdl.handle.net/10754/562475
Metadata
Show full item recordAbstract
This chapter is devoted to the study of an extension of dynamic programming approach that allows sequential optimization of exact decision rules relative to the length and coverage. It contains also results of experiments with decision tables from UCI Machine Learning Repository. © Springer-Verlag Berlin Heidelberg 2013.Citation
Amin, T., Chikalov, I., Moshkov, M., & Zielosko, B. (2013). Dynamic Programming Approach for Exact Decision Rule Optimization. Intelligent Systems Reference Library, 211–228. doi:10.1007/978-3-642-30344-9_6Publisher
Springer NatureISBN
9783642303432ae974a485f413a2113503eed53cd6c53
10.1007/978-3-642-30344-9_6