Extreme Learning Machines 2013_ Algorithms and Applications [Sun, Toh, Romay & Mao 2014-03-05].pdf

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Adaptation, Learning, and Optimization 16
Fuchen Sun
Kar-Ann Toh
Manuel Grana Romay
Kezhi Mao
Editors
Extreme Learning
Machines 2013:
Algorithms and
Applications
Adaptation, Learning, and Optimization
Volume 16
Series editors
Meng-Hiot Lim, Nanyang Technological University, Singapore
email: emhlim@ntu.edu.sg
Yew-Soon Ong, Nanyang Technological University, Singapore
email: asysong@ntu.edu.sg
For further volumes:
http://www.springer.com/series/8335
About this Series
The role of adaptation, learning and optimization are becoming increasingly
essential and intertwined. The capability of a system to adapt either through
modification of its physiological structure or via some revalidation process of
internal mechanisms that directly dictate the response or behavior is crucial in
many real world applications. Optimization lies at the heart of most machine
learning approaches while learning and optimization are two primary means to
effect adaptation in various forms. They usually involve computational processes
incorporated within the system that trigger parametric updating and knowledge or
model enhancement, giving rise to progressive improvement. This book series
serves as a channel to consolidate work related to topics linked to adaptation,
learning and optimization in systems and structures. Topics covered under this
series include:
•
complex adaptive systems including evolutionary computation, memetic com-
puting, swarm intelligence, neural networks, fuzzy systems, tabu search, sim-
ulated annealing, etc.
•
machine learning, data mining & mathematical programming
•
hybridization of techniques that span across artificial intelligence and compu-
tational intelligence for synergistic alliance of strategies for problem-solving.
•
aspects of adaptation in robotics
•
agent-based computing
•
autonomic/pervasive computing
•
dynamic optimization/learning in noisy and uncertain environment
•
systemic alliance of stochastic and conventional search techniques
•
all aspects of adaptations in man-machine systems.
This book series bridges the dichotomy of modern and conventional mathematical
and heuristic/meta-heuristics approaches to bring about effective adaptation,
learning and optimization. It propels the maxim that the old and the new can come
together and be combined synergistically to scale new heights in problem-solving.
To reach such a level, numerous research issues will emerge and researchers will
find the book series a convenient medium to track the progresses made.
Fuchen Sun Kar-Ann Toh
Manuel Grana Romay Kezhi Mao
•
•
Editors
Extreme Learning Machines
2013: Algorithms and
Applications
123
Editors
Fuchen Sun
Department of Computer Science
and Technology
Tsinghua University
Beijing
People’s Republic of China
Kar-Ann Toh
School of Electrical and Electronic
Engineering
Yonsei University
Seoul
Republic of Korea (South Korea)
Manuel Grana Romay
Department of Computer Science
and Artificial Intelligence
Universidad Del Pais Vasco
San Sebastian
Spain
Kezhi Mao
School of Electrical and Electronic
Engineering
Nanyang Technological University
Singapore
Singapore
ISSN 1867-4534
ISSN 1867-4542 (electronic)
ISBN 978-3-319-04740-9
ISBN 978-3-319-04741-6 (eBook)
DOI 10.1007/978-3-319-04741-6
Springer Cham Heidelberg New York Dordrecht London
Library of Congress Control Number: 2014933566
Ó
Springer International Publishing Switzerland 2014
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of
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