Ebook: Machine Learning in Medicine: Part Two
- Tags: Biomedicine general, Entomology, Medicine/Public Health general, Statistics general, Computer Imaging Vision Pattern Recognition and Graphics, Literacy
- Year: 2013
- Publisher: Springer Netherlands
- Edition: 1
- Language: English
- pdf
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.
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.
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.
Content:
Front Matter....Pages i-xiv
Introduction to Machine Learning Part Two....Pages 1-7
Two-Stage Least Squares....Pages 9-15
Multiple Imputations....Pages 17-26
Bhattacharya Analysis....Pages 27-38
Quality-of-Life (QOL) Assessments with Odds Ratios....Pages 39-44
Logistic Regression for Assessing Novel Diagnostic Tests Against Control....Pages 45-52
Validating Surrogate Endpoints....Pages 53-64
Two-Dimensional Clustering....Pages 65-75
Multidimensional Clustering....Pages 77-91
Anomaly Detection....Pages 93-103
Association Rule Analysis....Pages 105-113
Multidimensional Scaling....Pages 115-128
Correspondence Analysis....Pages 129-137
Multivariate Analysis of Time Series....Pages 139-153
Support Vector Machines....Pages 155-161
Bayesian Networks....Pages 163-170
Protein and DNA Sequence Mining....Pages 171-185
Continuous Sequential Techniques....Pages 187-194
Discrete Wavelet Analysis....Pages 195-206
Machine Learning and Common Sense....Pages 207-211
Back Matter....Pages 213-231
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.
Content:
Front Matter....Pages i-xiv
Introduction to Machine Learning Part Two....Pages 1-7
Two-Stage Least Squares....Pages 9-15
Multiple Imputations....Pages 17-26
Bhattacharya Analysis....Pages 27-38
Quality-of-Life (QOL) Assessments with Odds Ratios....Pages 39-44
Logistic Regression for Assessing Novel Diagnostic Tests Against Control....Pages 45-52
Validating Surrogate Endpoints....Pages 53-64
Two-Dimensional Clustering....Pages 65-75
Multidimensional Clustering....Pages 77-91
Anomaly Detection....Pages 93-103
Association Rule Analysis....Pages 105-113
Multidimensional Scaling....Pages 115-128
Correspondence Analysis....Pages 129-137
Multivariate Analysis of Time Series....Pages 139-153
Support Vector Machines....Pages 155-161
Bayesian Networks....Pages 163-170
Protein and DNA Sequence Mining....Pages 171-185
Continuous Sequential Techniques....Pages 187-194
Discrete Wavelet Analysis....Pages 195-206
Machine Learning and Common Sense....Pages 207-211
Back Matter....Pages 213-231
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