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Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions: 156 (Intelligent Systems Reference Library, 156) - Tapa dura
Sinopsis
The results presented here (including the assessment of a new tool - inhibitory trees) offer valuable tools for researchers in the areas of data mining, knowledge discovery, and machine learning, especially those whose work involves decision tables with many-valued decisions. The authors consider various examples of problems and corresponding decision tables with many-valued decisions, discuss the difference between decision and inhibitory trees and rules, and develop tools for their analysis and design. Applications include the study of totally optimal (optimal in relation to a number of criteria simultaneously) decision and inhibitory trees and rules; the comparison of greedy heuristics for tree and rule construction as single-criterion and bi-criteria optimization algorithms; and the development of a restricted multi-pruning approach used in classification and knowledge representation.
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The results presented here (including the assessment of a new tool inhibitory trees) offer valuable tools for researchers in the areas of data mining, knowledge discovery, and machine learning, especially those whose work involves decision tables with many-valued decisions. The authors consider various examples of problems and corresponding decision tables with many-valued decisions, discuss the difference between decision and inhibitory trees and rules, and develop tools for their analysis and design. Applications include the study of totally optimal (optimal in relation to a number of criteria simultaneously) decision and inhibitory trees and rules; the comparison of greedy heuristics for tree and rule construction as single-criterion and bi-criteria optimization algorithms; and the development of a restricted multi-pruning approach used in classification and knowledge representation.
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Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions: 156 (Intelligent Systems Reference Library, 156)
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Springer 27 M, 2019
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ISBN 13: 9783030128531
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Hardcover. Condición: Very Good. Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions: 156 (Intelligent Systems Reference Library, 156) This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. . Nº de ref. del artículo: 7719-9783030128531
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Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions: 156 (Intelligent Systems Reference Library, 156)
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Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions (Intelligent Systems Reference Library, 156)
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Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions
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Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions (Intelligent Systems Reference Library, 156)
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Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions
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ISBN 13: 9783030128531
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The results presented here (including the assessment of a new tool - inhibitory trees) offer valuable tools for researchers in the areas of data mining, knowledge discovery, and machine learning, especially those whose work involves decision tables with many-valued decisions. The authors consider various examples of problems and corresponding decision tables with many-valued decisions, discuss the difference between decision and inhibitory trees and rules, and develop tools for their analysis and design. Applications include the study of totally optimal (optimal in relation to a number of criteria simultaneously) decision and inhibitory trees and rules; the comparison of greedy heuristics for tree and rule construction as single-criterion and bi-criteria optimization algorithms; and the development of a restricted multi-pruning approach used in classification and knowledge representation. 296 pp. Englisch. Nº de ref. del artículo: 9783030128531
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