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

Moshkov, Mikhail; Zielosko, Beata

 
9783642269011: Combinatorial Machine Learning: A Rough Set Approach: 360 (Studies in Computational Intelligence)
Tapa blanda
ISBN 10: 364226901X ISBN 13: 9783642269011
Editorial: Springer, 2013

Sinopsis

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 It contains many unpublished results.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.

"Sinopsis" puede pertenecer a otra edición de este libro.

De la contraportada

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 It contains many unpublished results.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.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Springer
Año de publicación
2013
Idioma
Inglés
ISBN 10 
364226901X
ISBN 13 
9783642269011
Encuadernación
Tapa blanda
Número de páginas
196
Contacto del fabricante
no disponible
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Hamburg
Alemania

Otras ediciones populares con el mismo título

9783642209949: Combinatorial Machine Learning: A Rough Set Approach: 360 (Studies in Computational Intelligence)

Edición Destacada

ISBN 10:  3642209947 ISBN 13:  9783642209949
Editorial: Springer, 2011
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