Broad Learning Through Fusions - An Application on Social Networks (Paperback, 1st ed. 2019)

This book offers a clear and comprehensive introduction to broad learning, one of the novel learning problems studied in data mining and machine learning. Broad learning aims at fusing multiple large-scale information sources of diverse varieties together, and carrying out synergistic data mining tasks across these fused sources in one unified analytic. This book takes online social networks as an application example to introduce the latest alignment and knowledge discovery algorithms. Besides the overview of broad learning, machine learning and social network basics, specific topics covered in this book include network alignment, link prediction, community detection, information diffusion, viral marketing, and network embedding.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Release date:	August 2020
First published:	2019
Authors:	Jia Wei Zhang • Philip S. Yu
Dimensions:	254 x 178mm (L x W)
Format:	Paperback
Pages:	419
Edition:	1st ed. 2019
ISBN-13:	978-3-03-012530-1
Categories:	Books > Computing & IT > General theory of computing > Mathematical theory of computation Books > Computing & IT > General theory of computing > Data structures Books > Computing & IT > Computer programming > Algorithms & procedures Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematics for scientists & engineers Books > Computing & IT > Applications of computing > Databases > Data mining Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	3-03-012530-0
Barcode:	9783030125301

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