Machine Learning for Adaptive Many-Core Machines - Noel Lopes.pdf

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Studies in Big Data 7
Noel Lopes
Bernardete Ribeiro
Machine Learning
for Adaptive Many-
Core Machines –
A Practical Approach
Studies in Big Data
Volume 7
Series editor
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
e-mail: kacprzyk@ibspan.waw.pl
For further volumes:
http://www.springer.com/series/11970
About this Series
The series “Studies in Big Data” (SBD) publishes new developments and advances
in the various areas of Big Data- quickly and with a high quality. The intent is to
cover the theory, research, development, and applications of Big Data, as embedded
in the fields of engineering, computer science, physics, economics and life sciences.
The books of the series refer to the analysis and understanding of large, complex,
and/or distributed data sets generated from recent digital sources coming from sen-
sors or other physical instruments as well as simulations, crowd sourcing, social
networks or other internet transactions, such as emails or video click streams and
other. The series contains monographs, lecture notes and edited volumes in Big Data
spanning the areas of computational intelligence incl. neural networks, evolutionary
computation, soft computing, fuzzy systems, as well as artificial intelligence, data
mining, modern statistics and Operations research, as well as self-organizing sys-
tems. Of particular value to both the contributors and the readership are the short
publication timeframe and the world-wide distribution, which enable both wide and
rapid dissemination of research output.
Noel Lopes
·
Bernardete Ribeiro
Machine Learning
for Adaptive Many-Core
Machines – A Practical
Approach
ABC
Noel Lopes
Polytechnic Institute of Guarda
Guarda
Portugal
Bernardete Ribeiro
Department of Informatics Engineering
Faculty of Sciences and Technology
University of Coimbra, Polo II
Coimbra
Portugal
ISSN 2197-6503
ISBN 978-3-319-06937-1
DOI 10.1007/978-3-319-06938-8
ISSN 2197-6511 (electronic)
ISBN 978-3-319-06938-8 (eBook)
Springer Cham Heidelberg New York Dordrecht London
Library of Congress Control Number: 2014939947
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