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Moshkov, Mikhail; Zielosko, Beata - Combinatorial Machine Learning - 9783642209949 - V9783642209949
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Combinatorial Machine Learning

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Description for Combinatorial Machine Learning Hardback. 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. Series: Studies in Computational Intelligence. Num Pages: 182 pages, biography. BIC Classification: UYQM. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 12. Weight in Grams: 1000.

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. ... Read more

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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Product Details

Format
Hardback
Publication date
2011
Publisher
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Germany
Number of pages
182
Condition
New
Series
Studies in Computational Intelligence
Number of Pages
182
Place of Publication
Berlin, Germany
ISBN
9783642209949
SKU
V9783642209949
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15

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