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<title><![CDATA[Comentarios al libro: COMBINATORIAL MACHINE LEARNING]]></title>
<link><![CDATA[https://www.biblioeteca.com/biblioeteca.web/titulo/combinatorial-machine-learning]]></link>
<description><![CDATA[Decision trees and decision rule systems are widely used in different applications<p>as algorithms for problem solving, as predictors, and as a way for<p>knowledge representation. Reducts play key role in the problem of attribute<p>(feature) selection. The aims of this book are (i) the consideration of the sets<p>of decision trees, rules and reducts; (ii) study of relationships among these<p>objects; (iii) design of algorithms for construction of trees, rules and reducts;<p>and (iv) obtaining bounds on their complexity. Applications for supervised<p>machine learning, discrete optimization, analysis of acyclic programs, fault<p>diagnosis, and pattern recognition are considered also. This is a mixture of<p>research monograph and lecture notes. It contains many unpublished results.<p>However, proofs are carefully selected to be understandable for students.<p>The results considered in this book can be useful for researchers in machine<p>learning, data mining and knowledge discovery, especially for those who are<p>working in rough set theory, test theory and logical analysis of data. The book<p>can be used in the creation of courses for graduate students.]]></description>
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