Machine learning for adaptive many-core machines -- a practical approach [electronic resource] / Noel Lopes, Bernardete Ribeiro.
2015
Q325.5
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Title
Machine learning for adaptive many-core machines -- a practical approach [electronic resource] / Noel Lopes, Bernardete Ribeiro.
Author
Lopes, Noel, author.
ISBN
9783319069388 electronic book
3319069381 electronic book
9783319069371
3319069381 electronic book
9783319069371
Published
Cham : Springer, 2015.
Language
English
Description
1 online resource (xx, 241 pages) : illustrations (some color).
Item Number
10.1007/978-3-319-06938-8 doi
Call Number
Q325.5
Dewey Decimal Classification
006.3/1
Summary
The overwhelming data produced everyday and the increasing performance and cost requirements of applications is transversal to a wide range of activities in society, from science to industry. In particular, the magnitude and complexity of the tasks that Machine Learning (ML) algorithms have to solve are driving the need to devise adaptive many-core machines that scale well with the volume of data, or in other words, can handle Big Data. This book gives a concise view on how to extend the applicability of well-known ML algorithms in Graphics Processing Unit (GPU) with data scalability in mind. It presents a series of new techniques to enhance, scale and distribute data in a Big Learning framework. It is not intended to be a comprehensive survey of the state of the art of the whole field of machine learning for Big Data. Its purpose is less ambitious and more practical: to explain and illustrate existing and novel GPU-based ML algorithms, not viewed as a universal solution for the Big Data challenges but rather as part of the answer, which may require the use of different strategies coupled together.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Description based on online resource; title from PDF title page (SpringerLink, viewed July 7, 2014).
Added Author
Ribeiro, Bernardete, author.
Series
Studies in big data ; v.7.
Available in Other Form
Print version: 9783319069371
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Table of Contents
Introduction
Supervised Learning
Unsupervised and Semi-supervised Learning
Large-Scale Machine Learning.
Supervised Learning
Unsupervised and Semi-supervised Learning
Large-Scale Machine Learning.