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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 It contains many unpublished results.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 It contains many unpublished results.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.
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Combinatorial Machine Learning : A Rough Set Approach
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Combinatorial Machine Learning: A Rough Set Approach (Studies in Computational Intelligence, 360, Band 360) [Hardcover] Moshkov, Mikhail and Zielosko, Beata
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Combinatorial Machine Learning
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Hardcover. Condición: new. Hardcover. 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. Decision trees and decision rule systems are widely used in different applicationsas algorithms for problem solving, as predictors, and as a way forknowledge representation. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9783642209949
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Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2011
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ISBN 13: 9783642209949
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Hardcover. Condición: new. Hardcover. 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. Decision trees and decision rule systems are widely used in different applicationsas algorithms for problem solving, as predictors, and as a way forknowledge representation. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Nº de ref. del artículo: 9783642209949
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