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Rule based systems for big data = a ...

Liu, Han.

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Rule based systems for big data = a machine learning approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Rule based systems for big data/ by Han Liu, Alexander Gegov, Mihaela Cocea.
Reminder of title:	a machine learning approach /
Author:	Liu, Han.
other author:	Gegov, Alexander.
Published:	Cham :Springer International Publishing : : 2016.,
Description:	xiii, 121 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Introduction -- Theoretical Preliminaries -- Generation of Classification Rules -- Simplification of Classification Rules -- Representation of Classification Rules -- Ensemble Learning Approaches -- Interpretability Analysis.
Contained By:	Springer eBooks
Subject:	System design. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-23696-4
ISBN:	9783319236964$q(electronic bk.)

Rule based systems for big data = a machine learning approach /
Liu, Han.

Rule based systems for big dataa machine learning approach /[electronic resource] :by Han Liu, Alexander Gegov, Mihaela Cocea. - Cham :Springer International Publishing :2016. - xiii, 121 p. :ill. (some col.), digital ;24 cm. - Studies in big data,v.132197-6503 ;. - Studies in big data ;v.1..

Introduction -- Theoretical Preliminaries -- Generation of Classification Rules -- Simplification of Classification Rules -- Representation of Classification Rules -- Ensemble Learning Approaches -- Interpretability Analysis.

The ideas introduced in this book explore the relationships among rule based systems, machine learning and big data. Rule based systems are seen as a special type of expert systems, which can be built by using expert knowledge or learning from real data. The book focuses on the development and evaluation of rule based systems in terms of accuracy, efficiency and interpretability. In particular, a unified framework for building rule based systems, which consists of the operations of rule generation, rule simplification and rule representation, is presented. Each of these operations is detailed using specific methods or techniques. In addition, this book also presents some ensemble learning frameworks for building ensemble rule based systems.

ISBN: 9783319236964$q(electronic bk.)

Standard No.: 10.1007/978-3-319-23696-4doiSubjects--Topical Terms:

518843
System design.

LC Class. No.: QA76.9.S88

Dewey Class. No.: 004.21

Rule based systems for big data = a machine learning approach /
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245 1 0 $a Rule based systems for big data $h [electronic resource] : $b a machine learning approach / $c by Han Liu, Alexander Gegov, Mihaela Cocea.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a xiii, 121 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Studies in big data, $x 2197-6503 ; $v v.13
505 0 $a Introduction -- Theoretical Preliminaries -- Generation of Classification Rules -- Simplification of Classification Rules -- Representation of Classification Rules -- Ensemble Learning Approaches -- Interpretability Analysis.
520 $a The ideas introduced in this book explore the relationships among rule based systems, machine learning and big data. Rule based systems are seen as a special type of expert systems, which can be built by using expert knowledge or learning from real data. The book focuses on the development and evaluation of rule based systems in terms of accuracy, efficiency and interpretability. In particular, a unified framework for building rule based systems, which consists of the operations of rule generation, rule simplification and rule representation, is presented. Each of these operations is detailed using specific methods or techniques. In addition, this book also presents some ensemble learning frameworks for building ensemble rule based systems.
650 0 $a System design. $3 518843
650 0 $a Rule-based programming. $3 692014
650 0 $a Machine learning. $3 533906
650 0 $a Big data. $3 2045508
650 1 4 $a Engineering. $3 586835
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
700 1 $a Gegov, Alexander. $3 1244376
700 1 $a Cocea, Mihaela. $3 2179967
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856 4 0 $u http://dx.doi.org/10.1007/978-3-319-23696-4
950 $a Engineering (Springer-11647)