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Titlebook: Combinatorial Machine Learning; A Rough Set Approach Mikhail Moshkov,Beata Zielosko Book 2011 Springer Berlin Heidelberg 2011 Combinatorial

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发表于 2025-3-21 18:22:51 | 显示全部楼层 |阅读模式
书目名称Combinatorial Machine Learning
副标题A Rough Set Approach
编辑Mikhail Moshkov,Beata Zielosko
视频videohttp://file.papertrans.cn/230/229924/229924.mp4
概述A rough set approach to combinatorial machine learning.Presents applications for supervised machine learning, discrete optimization, analysis of acyclic programs, fault diagnosis and pattern recogniti
丛书名称Studies in Computational Intelligence
图书封面Titlebook: Combinatorial Machine Learning; A Rough Set Approach Mikhail Moshkov,Beata Zielosko Book 2011 Springer Berlin Heidelberg 2011 Combinatorial
描述.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 It contains many unpublished results..However, proofs are carefully selected to be understandable for students..The results considered in this book can be useful for researchers in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..
出版日期Book 2011
关键词Combinatorial Machine Learning; Computational Intelligence; Machine Learning; Rough Sets
版次1
doihttps://doi.org/10.1007/978-3-642-20995-6
isbn_softcover978-3-642-26901-1
isbn_ebook978-3-642-20995-6Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightSpringer Berlin Heidelberg 2011
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Sets of Tests, Decision Rules and Treessideration from decision tables with one-valued decisions. For simplicity, we deal mainly with decision tables containing only binary conditional attributes..This chapter is devoted to the study of the sets of tests (reducts), decision rules and trees. For tests and rules we concentrate on considera
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Bounds on Complexity of Tests, Decision Rules and Treesand tests..The chapter consists of three sections. In Sect. 3.1, we investigate lower bounds on the depth of decision trees, cardinality of tests and length of decision rules..Section 3.2 is devoted to the consideration of upper bounds on the minimum cardinality of tests and minimum depth of decisio
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Algorithms for Construction of Tests, Decision Rules and Treesdinality, decision rules with minimum length, and decision trees with minimum depth. Unfortunately, all the three optimization problems are .-hard. So we consider not only exact but also approximate algorithms for optimization..The chapter consists of four sections. In Sect. 4.1, we study approximat
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Decision Trees and Rules over Quasilinear Information Systemsptimization problems and analysis of acyclic programs in the basis . = {. + .,. − .,1;sign(.)}..Each problem over a linear information system can be represented in the following form. We take finite number of hyperplanes in the space ℝ.. These hyperplanes divide given part . of the space into domain
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Recognition of Words and Diagnosis of FaultsIn the first case, we study both decision trees and complete systems of decision rules. In the second case, we restrict our consideration to decision trees..Proofs are too complicated to be considered in this chapter. However, we give some comments relative to the tools used in the proofs or ideas o
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