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Machine learning is concerned with the analysis of large data and multiple variables. However, it is also often more sensitive than traditional statistical methods to analyze small data. The first volume reviewed subjects like optimal scaling, neural networks, factor analysis, partial least squares, discriminant analysis, canonical analysis, and fuzzy modeling. This second volume includes various clustering models, support vector machines, Bayesian networks, discrete wavelet analysis, genetic programming, association rule learning, anomaly detection, correspondence analysis, 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.This second of a two-volume work includes coverage of various clustering models, support vector machines, Bayesian networks, discrete wavelet analysis, genetic programming, association rule learning, anomaly detection and correspondence analysis.1 Introduction to Machine Learning Part Two. 2 Two-stage Least Squares. 3 Multiple Imputations. 4 Bhattacharya Analysis. 5 Quality-of-life (QOL) Assessments with Odds Ratios. 6 Logistic Regression for Assessing Novel Diagnostic Tests against Control.7 Validating Surrogate Endpoints. 8 Two-dimensional Clustering. 9 Multidimensional Clustering. 10 Anomaly Detection. 11 Association Rule Analysis. 12 Multidimensional Scaling. 13 Correspondence Analysis. 14 Multivariate Analysis of Tlw
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