Overview
- Recent research in machine learning for adaptive many-core machines
- Presents a practical approach
- Written by experts in the field
Part of the book series: Studies in Big Data (SBD, volume 7)
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Table of contents (9 chapters)
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Front Matter
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Part I- Introduction
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Front Matter
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Part II- Supervised Learning
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Front Matter
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Part III- Unsupervised and Semi-supervised Learning
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Front Matter
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Part IV- Large-Scale Machine Learning
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Front Matter
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Back Matter
Keywords
About this book
The overwhelming data produced everyday and the increasing performance and cost requirements of applications are 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.
Authors and Affiliations
Bibliographic Information
Book Title: Machine Learning for Adaptive Many-Core Machines - A Practical Approach
Authors: Noel Lopes, Bernardete Ribeiro
Series Title: Studies in Big Data
DOI: https://doi.org/10.1007/978-3-319-06938-8
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing Switzerland 2015
Hardcover ISBN: 978-3-319-06937-1Published: 16 July 2014
Softcover ISBN: 978-3-319-38096-4Published: 17 September 2016
eBook ISBN: 978-3-319-06938-8Published: 28 June 2014
Series ISSN: 2197-6503
Series E-ISSN: 2197-6511
Edition Number: 1
Number of Pages: XX, 241
Number of Illustrations: 108 b/w illustrations, 4 illustrations in colour
Topics: Computational Intelligence, Artificial Intelligence, Operations Research/Decision Theory