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Rule based systems for big data[electronic resource] :a machine learning approach /

纪录类型:	书目-语言数据,印刷品 : Monograph/item
[NT 15000414] null:	004.21
[NT 47271] Title/Author:	Rule based systems for big data : a machine learning approach // by Han Liu, Alexander Gegov, Mihaela Cocea.
作者:	Liu, Han.
[NT 51406] other author:	Gegov, Alexander.
出版者:	Cham : : Springer International Publishing :, 2016.
面页册数:	xiii, 121 p. : : ill. (some col.), digital ;; 24 cm.
Contained By:	Springer eBooks
标题:	System design.
标题:	Rule-based programming.
标题:	Machine learning.
标题:	Big data.
标题:	Engineering.
标题:	Computational Intelligence.
标题:	Artificial Intelligence (incl. Robotics)
标题:	Data Mining and Knowledge Discovery.
ISBN:	9783319236964
ISBN:	9783319236957
[NT 15000228] null:	Introduction -- Theoretical Preliminaries -- Generation of Classification Rules -- Simplification of Classification Rules -- Representation of Classification Rules -- Ensemble Learning Approaches -- Interpretability Analysis.
[NT 15000229] null:	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.
电子资源:	http://dx.doi.org/10.1007/978-3-319-23696-4

Rule based systems for big data[electronic resource] :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.7..

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

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

172186
System design.

LC Class. No.: QA76.9.S88

Dewey Class. No.: 004.21

Rule based systems for big data[electronic resource] :a machine learning approach /
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