Bounds on Complexity and Algorithms for Construction of Nondeterministic and Strongly Nondeterministic Decision Trees for Decision Tables

Mikhail Moshkov

doi:10.1007/978-3-030-41728-4_6

Bounds on Complexity and Algorithms for Construction of Nondeterministic and Strongly Nondeterministic Decision Trees for Decision Tables

Mikhail Moshkov^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

In this chapter, we consider bounds on the minimum complexity, an approach to proof of lower bounds, and algorithms for construction of nondeterministic and strongly nondeterministic decision trees. The bounds on complexity are true for arbitrary complexity functions. The approach to proof of lower bounds assumes that the complexity functions have the property The considered algorithms require complexity functions having properties.

Original language	English (US)
Title of host publication	Intelligent Systems Reference Library
Publisher	Springer
Pages	85-91
Number of pages	7
DOIs	https://doi.org/10.1007/978-3-030-41728-4_6
State	Published - 2020

Publication series

Name	Intelligent Systems Reference Library
Volume	179
ISSN (Print)	1868-4394
ISSN (Electronic)	1868-4408

ASJC Scopus subject areas

General Computer Science
Information Systems and Management
Library and Information Sciences

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10.1007/978-3-030-41728-4_6

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title = "Bounds on Complexity and Algorithms for Construction of Nondeterministic and Strongly Nondeterministic Decision Trees for Decision Tables",

abstract = "In this chapter, we consider bounds on the minimum complexity, an approach to proof of lower bounds, and algorithms for construction of nondeterministic and strongly nondeterministic decision trees. The bounds on complexity are true for arbitrary complexity functions. The approach to proof of lower bounds assumes that the complexity functions have the property The considered algorithms require complexity functions having properties.",

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AB - In this chapter, we consider bounds on the minimum complexity, an approach to proof of lower bounds, and algorithms for construction of nondeterministic and strongly nondeterministic decision trees. The bounds on complexity are true for arbitrary complexity functions. The approach to proof of lower bounds assumes that the complexity functions have the property The considered algorithms require complexity functions having properties.

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Bounds on Complexity and Algorithms for Construction of Nondeterministic and Strongly Nondeterministic Decision Trees for Decision Tables

Abstract

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