國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Rule based systems for big data/ by Han Liu, Alexander Gegov, Mihaela Cocea.
其他題名:	a machine learning approach /
作者:	Liu, Han.
其他作者:	Gegov, Alexander.
出版者:	Cham :Springer International Publishing : : 2016.,
面頁冊數:	xiii, 121 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer eBooks
標題:	System design. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-23696-4
ISBN:	9783319236964

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

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

560330
System design.

LC Class. No.: QA76.9.S88

Dewey Class. No.: 004.21

Rule based systems for big data = a machine learning approach /
LDR:02002nam a2200325 a 4500 001 860818
003 DE-He213
005 20160721145615.0
006 m d
007 cr nn 008maaau
008 170720s2016 gw s 0 eng d
020 $a 9783319236964 $q (electronic bk.)
020 $a 9783319236957 $q (paper)
024 7 $a 10.1007/978-3-319-23696-4 $2 doi
035 $a 978-3-319-23696-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.S88
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 004.21 $2 23
090 $a QA76.9.S88 $b L783 2016
100 1 $a Liu, Han. $3 1102745
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 : $c 2016. $b Springer International Publishing : $b Imprint: Springer,
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 560330
650 0 $a Rule-based programming. $3 909907
650 0 $a Machine learning. $3 561253
650 0 $a Big data. $3 981821
650 1 4 $a Engineering. $3 561152
650 2 4 $a Computational Intelligence. $3 768837
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 593924
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
700 1 $a Gegov, Alexander. $3 1102746
700 1 $a Cocea, Mihaela. $3 1102747
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
830 0 $a Studies in big data ; $v v.1. $3 1020233
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-23696-4
950 $a Engineering (Springer-11647)