EAN: 9783319172897

MACHINE LEARNING IN COMPLEX NETWORKS - THIAGO C...

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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