Rule Based Systems for Big Data - A Machine Learning Approach (Paperback, Softcover reprint of the original 1st ed. 2016)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Studies in Big Data, 13
Release date:	August 2016
First published:	2016
Authors:	Han Liu • Alexander Gegov • Mihaela Cocea
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	121
Edition:	Softcover reprint of the original 1st ed. 2016
ISBN-13:	978-3-319-37027-9
Categories:	Books > Computing & IT > Applications of computing > Databases > Data mining Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	3-319-37027-8
Barcode:	9783319370279

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