東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Comparative analysis of deterministi...

Moshkov, Mikhail.

Linked to FindBook

Google Book

Amazon

博客來

Comparative analysis of deterministic and nondeterministic decision trees

Record Type:	Electronic resources : Monograph/item
Title/Author:	Comparative analysis of deterministic and nondeterministic decision trees/ by Mikhail Moshkov.
Author:	Moshkov, Mikhail.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	xvi, 297 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Basic Deﬁnitions and Notation -- Lower Bounds on Complexity of Deterministic Decision Trees for Decision Tables -- Upper Bounds on Complexity and Algorithms for Construction of Deterministic Decision Trees for Decision Tables -- Bounds on Complexity and Algorithms for Construction of Nondeterministic and Strongly Nondeterministic Decision Trees for Decision Tables -- Closed Classes of Boolean Functions -- Algorithmic Problems -- Basic Deﬁnitions and Notation -- Main Reductions -- Functions on Main Diagonal and Below -- Local Upper Types of Restricted Sccf-Triples -- Bounds Inside Types.
Contained By:	Springer eBooks
Subject:	Decision trees. -
Online resource:	https://doi.org/10.1007/978-3-030-41728-4
ISBN:	9783030417284

Comparative analysis of deterministic and nondeterministic decision trees
Moshkov, Mikhail.

Comparative analysis of deterministic and nondeterministic decision trees[electronic resource] /by Mikhail Moshkov. - Cham :Springer International Publishing :2020. - xvi, 297 p. :ill., digital ;24 cm. - Intelligent systems reference library,v.1791868-4394 ;. - Intelligent systems reference library ;v.179..

Introduction -- Basic Deﬁnitions and Notation -- Lower Bounds on Complexity of Deterministic Decision Trees for Decision Tables -- Upper Bounds on Complexity and Algorithms for Construction of Deterministic Decision Trees for Decision Tables -- Bounds on Complexity and Algorithms for Construction of Nondeterministic and Strongly Nondeterministic Decision Trees for Decision Tables -- Closed Classes of Boolean Functions -- Algorithmic Problems -- Basic Deﬁnitions and Notation -- Main Reductions -- Functions on Main Diagonal and Below -- Local Upper Types of Restricted Sccf-Triples -- Bounds Inside Types.

This book compares four parameters of problems in arbitrary information systems: complexity of problem representation and complexity of deterministic, nondeterministic, and strongly nondeterministic decision trees for problem solving. Deterministic decision trees are widely used as classifiers, as a means of knowledge representation, and as algorithms. Nondeterministic (strongly nondeterministic) decision trees can be interpreted as systems of true decision rules that cover all objects (objects from one decision class) This book develops tools for the study of decision trees, including bounds on complexity and algorithms for construction of decision trees for decision tables with many-valued decisions. It considers two approaches to the investigation of decision trees for problems in information systems: local, when decision trees can use only attributes from the problem representation; and global, when decision trees can use arbitrary attributes from the information system. For both approaches, it describes all possible types of relationships among the four parameters considered and discusses the algorithmic problems related to decision tree optimization. The results presented are useful for researchers who apply decision trees and rules to algorithm design and to data analysis, especially those working in rough set theory, test theory and logical analysis of data. This book can also be used as the basis for graduate courses.

ISBN: 9783030417284

Standard No.: 10.1007/978-3-030-41728-4doiSubjects--Topical Terms:

827433
Decision trees.

LC Class. No.: QA166.2 / .M674 2020

Dewey Class. No.: 511.5

Comparative analysis of deterministic and nondeterministic decision trees
LDR:03140nmm a2200337 a 4500 001 2217122
003 DE-He213
005 20200804171026.0
006 m d
007 cr nn 008maaau
008 201120s2020 sz s 0 eng d
020 $a 9783030417284 $q (electronic bk.)
020 $a 9783030417277 $q (paper)
024 7 $a 10.1007/978-3-030-41728-4 $2 doi
035 $a 978-3-030-41728-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QA166.2 $b .M674 2020
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 511.5 $2 23
090 $a QA166.2 $b .M911 2020
100 1 $a Moshkov, Mikhail. $3 3450098
245 1 0 $a Comparative analysis of deterministic and nondeterministic decision trees $h [electronic resource] / $c by Mikhail Moshkov.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a xvi, 297 p. : $b ill., digital ; $c 24 cm.
490 1 $a Intelligent systems reference library, $x 1868-4394 ; $v v.179
505 0 $a Introduction -- Basic Deﬁnitions and Notation -- Lower Bounds on Complexity of Deterministic Decision Trees for Decision Tables -- Upper Bounds on Complexity and Algorithms for Construction of Deterministic Decision Trees for Decision Tables -- Bounds on Complexity and Algorithms for Construction of Nondeterministic and Strongly Nondeterministic Decision Trees for Decision Tables -- Closed Classes of Boolean Functions -- Algorithmic Problems -- Basic Deﬁnitions and Notation -- Main Reductions -- Functions on Main Diagonal and Below -- Local Upper Types of Restricted Sccf-Triples -- Bounds Inside Types.
520 $a This book compares four parameters of problems in arbitrary information systems: complexity of problem representation and complexity of deterministic, nondeterministic, and strongly nondeterministic decision trees for problem solving. Deterministic decision trees are widely used as classifiers, as a means of knowledge representation, and as algorithms. Nondeterministic (strongly nondeterministic) decision trees can be interpreted as systems of true decision rules that cover all objects (objects from one decision class) This book develops tools for the study of decision trees, including bounds on complexity and algorithms for construction of decision trees for decision tables with many-valued decisions. It considers two approaches to the investigation of decision trees for problems in information systems: local, when decision trees can use only attributes from the problem representation; and global, when decision trees can use arbitrary attributes from the information system. For both approaches, it describes all possible types of relationships among the four parameters considered and discusses the algorithmic problems related to decision tree optimization. The results presented are useful for researchers who apply decision trees and rules to algorithm design and to data analysis, especially those working in rough set theory, test theory and logical analysis of data. This book can also be used as the basis for graduate courses.
650 0 $a Decision trees. $3 827433
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Control and Systems Theory. $3 3381515
650 2 4 $a Data Engineering. $3 3409361
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Intelligent systems reference library ; $v v.179. $3 3450099
856 4 0 $u https://doi.org/10.1007/978-3-030-41728-4
950 $a Intelligent Technologies and Robotics (Springer-42732)