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

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書目名稱Combinatorial Machine Learning影響因子(影響力)




書目名稱Combinatorial Machine Learning影響因子(影響力)學(xué)科排名




書目名稱Combinatorial Machine Learning網(wǎng)絡(luò)公開度




書目名稱Combinatorial Machine Learning網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Combinatorial Machine Learning被引頻次




書目名稱Combinatorial Machine Learning被引頻次學(xué)科排名




書目名稱Combinatorial Machine Learning年度引用




書目名稱Combinatorial Machine Learning年度引用學(xué)科排名




書目名稱Combinatorial Machine Learning讀者反饋




書目名稱Combinatorial Machine Learning讀者反饋學(xué)科排名

Generalize

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

惡臭

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

FAR

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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泰然自若

FACT

遺留之物

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 ?.. 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