Rule Based Systems for Big Data (Record no. 51792)

000 -LEADER
fixed length control field 02764nam a22005295i 4500
001 - CONTROL NUMBER
control field 978-3-319-23696-4
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20200420220219.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 150909s2016 gw | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783319236964
-- 978-3-319-23696-4
082 04 - CLASSIFICATION NUMBER
Call Number 006.3
100 1# - AUTHOR NAME
Author Liu, Han.
245 10 - TITLE STATEMENT
Title Rule Based Systems for Big Data
Sub Title A Machine Learning Approach /
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2015.
300 ## - PHYSICAL DESCRIPTION
Number of Pages XIII, 121 p. 38 illus., 5 illus. in color.
490 1# - SERIES STATEMENT
Series statement Studies in Big Data,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Introduction -- Theoretical Preliminaries -- Generation of Classification Rules -- Simplification of Classification Rules -- Representation of Classification Rules -- Ensemble Learning Approaches -- Interpretability Analysis.
520 ## - SUMMARY, ETC.
Summary, etc 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.
700 1# - AUTHOR 2
Author 2 Gegov, Alexander.
700 1# - AUTHOR 2
Author 2 Cocea, Mihaela.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://dx.doi.org/10.1007/978-3-319-23696-4
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2016.
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-- text
-- txt
-- rdacontent
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-- computer
-- c
-- rdamedia
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-- online resource
-- cr
-- rdacarrier
347 ## -
-- text file
-- PDF
-- rda
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Engineering.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data mining.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial intelligence.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational intelligence.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Engineering.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational Intelligence.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial Intelligence (incl. Robotics).
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data Mining and Knowledge Discovery.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 2197-6503 ;
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-- ZDB-2-ENG

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