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Machine Learning in Medicine : Part Three / by Ton J. Cleophas, Aeilko H. Zwinderman.

Author/Creator:
Cleophas, Ton J. author., Author,
Publication:
Dordrecht : Springer Netherlands : Imprint: Springer, 2013.
Format/Description:
Book
1 online resource (XIX, 224 pages) : 41 illustrations
Edition:
1st ed. 2013.
Series:
Biomedical and Life Sciences (Springer-11642)
Contained In:
Springer eBooks
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Details

Subjects:
Medicine.
Statistics.
Optical data processing.
Local subjects:
Biomedicine, general. (search)
Medicine/Public Health, general. (search)
Statistics, general. (search)
Computer Imaging, Vision, Pattern Recognition and Graphics. (search)
System Details:
text file PDF
Summary:
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.
Contents:
Preface
Introduction to Machine Learning Part Three.- Evolutionary Operations.- Multiple Treatments
Multiple Endpoints
Optimal Binning
Exact P-Values
Probit Regression
Over - dispersion.10 Random Effects
Weighted Least Squares
Multiple Response Sets
Complex Samples
Runs Tests.- Decision Trees
Spectral Plots
Newton's Methods
Stochastic Processes, Stationary Markov Chains
Stochastic Processes, Absorbing Markov Chains
Conjoint Models
Machine Learning and Unsolved Questions
Index.
Contributor:
Zwinderman, Aeilko H. author., Author,
SpringerLink (Online service)
Other format:
Printed edition:
Printed edition:
Printed edition:
ISBN:
978-94-007-7869-6
9789400778696
Publisher Number:
10.1007/978-94-007-7869-6 doi
Access Restriction:
Restricted for use by site license.