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.
