Combinatorial Machine Learning: A Rough Set Approach (Studies in Computational Intelligence (360))

Moshkov, Mikhail, Zielosko, Beata

ISBN 10: 364226901X / ISBN 13: 9783642269011

Published by Springer, 2013

About this Item

Like New. Seller Inventory # ERICA773364226901X6

Bibliographic Details

Title: Combinatorial Machine Learning: A Rough Set ...

Publisher: Springer

Publication Date: 2013

Binding: Paperback

Condition: Like New

Book Type: book

About this title

Synopsis:

Decision trees and decision rule systems are widely used in different applications

as algorithms for problem solving, as predictors, and as a way for

knowledge representation. Reducts play key role in the problem of attribute

(feature) selection. The aims of this book are (i) the consideration of the sets

of decision trees, rules and reducts; (ii) study of relationships among these

objects; (iii) design of algorithms for construction of trees, rules and reducts;

and (iv) obtaining bounds on their complexity. Applications for supervised

machine learning, discrete optimization, analysis of acyclic programs, fault

diagnosis, and pattern recognition are considered also. This is a mixture of

research 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 It contains many unpublished results.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 machine

learning, data mining and knowledge discovery, especially for those who are

working in rough set theory, test theory and logical analysis of data. The book

can be used in the creation of courses for graduate students.