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

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Decision Trees and Rules over Quasilinear Information Systemsf domain which contains this point. Decision trees and decision rules over the considered information system use attributes of the kind .. This attribute allows us to recognize the position of a point relative to the hyperplane defined by the equality ∑?.?+?.?=?0.

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Institution

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EEG and Sleep During Developmentf proofs..This chapter consists of three sections. In Sect. 10.1, the problem of regular language word recognition is studied. Section 10.2 is devoted to the problem of diagnosis of constant faults in combinatorial circuits. Section 10.3 contains conclusions.

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愛(ài)花花兒憤怒

paroxysm

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Matt T. Bianchi M.D., Ph.D., M.M.Sc.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 decision trees. These bounds can be used also as upper bounds on the minimum length of decision rules..Section 3.3 contains conclusions.

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Bounds on Complexity of Tests, Decision Rules and Treeslength of decision rules..Section 3.2 is devoted to the consideration of upper bounds on the minimum cardinality of tests and minimum depth of decision trees. These bounds can be used also as upper bounds on the minimum length of decision rules..Section 3.3 contains conclusions.

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Decision Tables with Many-Valued Decisionsever, the problems of finding an arbitrary decision or one of the most frequent decisions from the generalized decision look also reasonable..This chapter consists of ten sections. Section 5.1 contains examples of decision tables with many-valued decisions. In Sect. 5.2, main notions are discussed.