Moshkov / Zielosko

Combinatorial Machine Learning

A Rough Set Approach
2013. Buch. xiv, 182 S.: Bibliographien. Softcover
Springer ISBN 978-3-642-26901-1
Format (B x L): 15,5 x 23,5 cm
Gewicht: 308 g
In englischer Sprache
Das Werk ist Teil der Reihe:
Decision trees and decision rule systems are widely used in different applicationsas algorithms for problem solving, as predictors, and as a way forknowledge representation. Reducts play key role in the problem of attribute(feature) selection. The aims of this book are (i) the consideration of the setsof decision trees, rules and reducts; (ii) study of relationships among theseobjects; (iii) design of algorithms for construction of trees, rules and reducts;and (iv) obtaining bounds on their complexity. Applications for supervisedmachine learning, discrete optimization, analysis of acyclic programs, faultdiagnosis, and pattern recognition are considered also. This is a mixture ofresearch monograph and lecture notes. It contains many unpublished results.However, proofs are carefully selected to be understandable for students.The results considered in this book can be useful for researchers in machinelearning, data mining and knowledge discovery, especially for those who areworking in rough set theory, test theory and logical analysis of data. The bookcan be used in the creation of courses for graduate students.



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A rough set approach to combinatorial machine learning Presents applications for supervised machine learning, discrete optimization, analysis of acyclic programs, fault diagnosis and pattern recognition Written by leading experts in the field