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Books > Computing & IT > Applications of computing > Artificial intelligence
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Foundations of Computational Intelligence - Volume 1: Learning and Approximation (Paperback, Softcover reprint of the original 1st ed. 2009)
Loot Price: R2,684
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Foundations of Computational Intelligence - Volume 1: Learning and Approximation (Paperback, Softcover reprint of the original 1st ed. 2009)
Series: Studies in Computational Intelligence, 201
Expected to ship within 18 - 22 working days
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Foundations of Computational Intelligence Volume 1: Learning and
Approximation: Theoretical Foundations and Applications Learning
methods and approximation algorithms are fundamental tools that
deal with computationally hard problems and problems in which the
input is gradually disclosed over time. Both kinds of problems have
a large number of applications arising from a variety of fields,
such as algorithmic game theory, approximation classes, coloring
and partitioning, competitive analysis, computational finance, cuts
and connectivity, inapproximability results, mechanism design,
network design, packing and covering, paradigms for design and
analysis of approxi- tion and online algorithms, randomization
techniques, real-world applications, scheduling problems and so on.
The past years have witnessed a large number of interesting
applications using various techniques of Computational Intelligence
such as rough sets, connectionist learning; fuzzy logic;
evolutionary computing; artificial immune systems; swarm
intelligence; reinforcement learning, intelligent multimedia
processing etc. . In spite of numerous successful applications of
C- putational Intelligence in business and industry, it is
sometimes difficult to explain the performance of these techniques
and algorithms from a theoretical perspective. Therefore, we
encouraged authors to present original ideas dealing with the inc-
poration of different mechanisms of Computational Intelligent
dealing with Lea- ing and Approximation algorithms and underlying
processes. This edited volume comprises 15 chapters, including an
overview chapter, which provides an up-to-date and state-of-the art
research on the application of Computational Intelligence for
learning and approximation.
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