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Machine Learning in Complex Networks (Paperback, Softcover reprint of the original 1st ed. 2016)
Loot Price: R3,196
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Machine Learning in Complex Networks (Paperback, Softcover reprint of the original 1st ed. 2016)
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This book presents the features and advantages offered by complex
networks in the machine learning domain. In the first part, an
overview on complex networks and network-based machine learning is
presented, offering necessary background material. In the second
part, we describe in details some specific techniques based on
complex networks for supervised, non-supervised, and
semi-supervised learning. Particularly, a stochastic particle
competition technique for both non-supervised and semi-supervised
learning using a stochastic nonlinear dynamical system is described
in details. Moreover, an analytical analysis is supplied, which
enables one to predict the behavior of the proposed technique. In
addition, data reliability issues are explored in semi-supervised
learning. Such matter has practical importance and is not often
found in the literature. With the goal of validating these
techniques for solving real problems, simulations on broadly
accepted databases are conducted. Still in this book, we present a
hybrid supervised classification technique that combines both low
and high orders of learning. The low level term can be implemented
by any classification technique, while the high level term is
realized by the extraction of features of the underlying network
constructed from the input data. Thus, the former classifies the
test instances by their physical features, while the latter
measures the compliance of the test instances with the pattern
formation of the data. We show that the high level technique can
realize classification according to the semantic meaning of the
data. This book intends to combine two widely studied research
areas, machine learning and complex networks, which in turn will
generate broad interests to scientific community, mainly to
computer science and engineering areas.
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