Combinatorial Machine Learning : A Rough Set Approach

Mikhail Moshkov

ISBN 10: 3642209947 / ISBN 13: 9783642209949

Edité par Springer, Berlin, Springer Berlin Heidelberg, Springer, 2011

Neuware - 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 It contains many unpublished results.However, proofs are carefully selected to be understandable for students.The results considered in this book can be useful for researchers in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. N° de réf. du vendeur 9783642209949

Synopsis :

This book explores decision trees and decision rule systems, rules and reducts, examines relationships among these objects and reviews the design of algorithms for construction of trees, rules and reducts. Includes carefully selected illustrative proofs.

Détails bibliographiques

Titre : Combinatorial Machine Learning : A Rough Set...
Éditeur : Springer, Berlin, Springer Berlin Heidelberg, Springer
Date d'édition : 2011
Reliure : Buch
Etat : Neu