Machine learning is concerned with the analysis of large data and
multiple variables. It is also often more sensitive than
traditional statistical methods to analyze small data. The first
and second volumes reviewed subjects like optimal scaling, neural
networks, factor analysis, partial least squares, discriminant
analysis, canonical analysis, fuzzy modeling, various clustering
models, support vector machines, Bayesian networks, discrete
wavelet analysis, association rule learning, anomaly detection, and
correspondence analysis. This third volume addresses more advanced
methods and includes subjects like evolutionary programming,
stochastic methods, complex sampling, optional binning, Newton's
methods, decision trees, and other subjects. Both the theoretical
bases and the step by step analyses are described for the benefit
of non-mathematical readers. Each chapter can be studied without
the need to consult other chapters. Traditional statistical tests
are, sometimes, priors to machine learning methods, and they are
also, sometimes, used as contrast tests. To those wishing to obtain
more knowledge of them, we recommend to additionally study (1)
Statistics Applied to Clinical Studies 5th Edition 2012, (2) SPSS
for Starters Part One and Two 2012, and (3) Statistical Analysis of
Clinical Data on a Pocket Calculator Part One and Two 2012, written
by the same authors, and edited by Springer, New York.
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