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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.
Inhaltsverzeichnis
Introduction to Machine Learning Part Two.- Two-stage Least Squares.- Multiple Imputations.- Bhattacharya Analysis.- Quality-of-life (QOL) Assessments with Odds Ratios.- Logistic Regression for Assessing Novel Diagnostic Tests against Control.- Validating Surrogate Endpoints.- Two-dimensional Clustering.- Multidimensional Clustering.- Anomaly Detection.- Association Rule Analysis.- Multidimensional Scaling.- Correspondence Analysis.- Multivariate Analysis of Time Series.- Support Vector Machines.- Bayesian Networks.- Protein and DNA Sequence Mining.- Continuous Sequential Techniques.- Discrete Wavelet Analysis.- Machine Learning and Common Sense.- Statistical Tables.- Index.
Zusammenfassung
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.
Zusatztext
From the reviews:
“This is the second volume of a novel publication on machine learning in medicine that details statistical analysis of complex data with many variables. … The intended audience includes physicians, clinical researchers, physicians in training, statisticians, and medical students as well as master’s and doctoral students in biostatistics and epidemiology. … The simple language and well-organized chapters are unsurpassed attributes of this book. It is an exceptional resource for a quick review of machine learning in medicine.” (Goral Panchal, Doody’s Book Reviews, October, 2013)
Bericht
From the reviews:
"This is the second volume of a novel publication on machine learning in medicine that details statistical analysis of complex data with many variables. ... The intended audience includes physicians, clinical researchers, physicians in training, statisticians, and medical students as well as master's and doctoral students in biostatistics and epidemiology. ... The simple language and well-organized chapters are unsurpassed attributes of this book. It is an exceptional resource for a quick review of machine learning in medicine." (Goral Panchal, Doody's Book Reviews, October, 2013)
