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

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Bibliographic Details
Author / Creator:Liu, Han, author.
Imprint:Cham : Springer, [2015]
©2016
Description:1 online resource (xiii, 121 pages) : illustrations (some color)
Language:English
Series:Studies in big data, 2197-6503 ; volume 13
Studies in big data ; volume 13.
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11248042
Hidden Bibliographic Details
Other authors / contributors:Gegov, Alexander, author.
Cocea, Mihaela, author.
ISBN:9783319236964
3319236962
9783319236957
3319236954
Notes:Includes bibliographical references.
Online resource; title from PDF title page (SpringerLink, viewed September 16, 2015).
Summary: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.
Other form:Print version: Liu, Han. Rule based systems for big data. Cham : Springer, [2015] 3319236954 9783319236957