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This monograph presents a theoretical framework for combinatorial machine learning grounded in the rough set approach. It develops methods for constructing and analyzing decision algorithms, decision rules, and decomposition techniques, with an emphasis on complexity evaluation and optimization. Positioned within the Studies in Computational Intelligence series, the book offers rigorous mathematical treatments useful for researchers working on data analysis, knowledge discovery, and computational learning theory.

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245: $a: Combinatorial Machine Learning: A Rough Set Approach
245: $b:
082: $a:
100: $a: Mikhail Moshkov & Beata Zielosko
650: $a: Machine Learning
250: $a:
440: $v:
250: $b: Springer (Springer‑Verlag Berlin Heidelberg)
250: $c: 2011
020: $a: 978-3-642-20994-9
300: $a: 184