Combinatorial machine learning: a rough set approach

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 It contains many unpublished results.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. A rough set approach to combinatorial machine learning. Presents applications for supervised machine learning, discrete optimization, analysis of acyclic programs, fault diagnosis and pattern recognition. Written by leading experts in the field INDICE: Part I Tools. Part II Applications.

• ISBN: 978-3-642-20994-9
• Editorial: Springer Berlin Heidelberg
• Encuadernacion: Cartoné
• Páginas: 190
• Fecha Publicación: 30/06/2011
• Nº Volúmenes: 1
• Idioma: Inglés