Title Scaling up machine learning : parallel and distributed approaches / edited by Ron Bekkerman, Mikhail Bilenko, John Langford.

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Online eBook via EBSCO. Access restricted to current Rider University students, faculty, and staff.
Instructions for reading/downloading the EBSCO version of this eBook

Item Status

Description	1 online resource (1 volume) : illustrations
	text file
Bibliography	Includes bibliographical references and index.
Summary	"This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs, and constraints of the available options"-- Provided by publisher.
Local Note	eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America
Subject	Machine learning.
	Machine learning.
	Data mining.
	Data mining.
	Parallel algorithms.
	Parallel algorithms.
	Parallel programs (Computer programs)
	Parallel programs (Computer programs)
Genre/Form	Electronic books.
Added Author	Bekkerman, Ron.
	Bilenko, Mikhail, 1978-
	Langford, John, 1975-
Other Form:	Print version: Scaling up machine learning. Cambridge ; New York : Cambridge University Press, 2012 9780521192248 (DLC) 2011016323 (OCoLC)728102114
ISBN	0521192242 (hardback)
	9780521192248 (hardback)
	9781139223461 (electronic book)
	1139223461 (electronic book)
	9780521192248
Standard No.	9786613579737