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by Steven Simske
Meta-Analytics
Cover image
Title page
Table of Contents
Copyright
Dedication
Acknowledgments
Chapter 1: Introduction, overview, and applications
Abstract
1.1 Introduction
1.2 Why is this book important?
1.3 Organization of the book
1.4 Informatics
1.5 Statistics for analytics
1.6 Algorithms for analytics
1.7 Machine learning
1.8 Artificial intelligence
1.9 A platform for building a classifier from the ground up (binary case)
1.10 A platform for building a classifier from the ground up (general case)
1.11 Summary
Chapter 2: Ground truthing
Abstract
2.1 Introduction
2.2 Pre-validation
2.3 Optimizing settings from training data
2.4 Learning how to Learn
2.5 Deep learning to deep unlearning
2.6 Summary
Chapter 3: Experimental design
Abstract
3.1 Introduction
3.2 Data normalization
3.3 Designs for the pruning of aging data
3.4 Systems of systems
3.5 Summary
Chapter 4: Meta-analytic design patterns
Abstract
4.1 Introduction
4.2 Cumulative response patterns
4.3 Optimization of analytics
4.4 Model agreement patterns
4.5 Co-occurrence and similarity patterns
4.6 Sensitivity analysis patterns
4.7 Confusion matrix patterns
4.8 Entropy patterns
4.9 Independence pattern
4.10 Functional NLP patterns (macro-feedback)
4.11 Summary
Chapter 5: Sensitivity analysis and big system engineering
Abstract
5.1 Introduction
5.2 Sensitivity analysis of the data set itself
5.3 Sensitivity analysis of the solution model
5.4 Sensitivity analysis of the individual algorithms
5.5 Sensitivity analysis of the hybrid algorithmics
5.6 Sensitivity analysis of the path to the current state
5.7 Summary
Chapter 6: Multipatch predictive selection
Abstract
6.1 Introduction
6.2 Predictive selection
6.3 Means of predicting
6.4 Means of selecting
6.5 Multi-path approach
6.6 Applications
6.7 Sensitivity analysis
6.8 Summary
Chapter 7: Modeling and model fitting
Abstract
7.1 Introduction
7.2 Chemistry analogues for analytics
7.3 Organic chemistry analogues for analytics
7.4 Immunological and biological analogues for analytics
7.5 Anonymization analogues for model design and fitting
7.6 LSE, error variance, and entropy: Goodness of fit
7.7 Make mine multiple models!
7.8 Summary
Chapter 8: Synonym-antonym and reinforce-void patterns
Abstract
8.1 Introduction
8.2 Synonym-antonym patterns
8.3 Reinforce-void patterns
8.4 Broader applicability of these patterns
8.5 Summary
Chapter 9: Analytics around analytics
Abstract
9.1 Introduction
9.2 Analytics around analytics
9.3 Optimizing settings from training data
9.4 Hybrid methods
9.5 Other areas for investigation around the analytics
9.6 Summary
Chapter 10: System design optimization
Abstract
10.1 Introduction
10.2 Module optimization
10.3 Clustering and regularization
10.4 Analytic system optimization
10.5 Summary
Chapter 11: Aleatory and expert system techniques
Abstract
11.1 Introduction
11.2 Revisiting two earlier aleatory patterns
11.3 Adding random elements for testing
11.4 Hyperspectral aleatory approaches
11.5 Other aleatory applications in machine and statistical learning
11.6 Expert system techniques
11.7 Summary
Chapter 12: Application I: Topics and challenges in machine translation, robotics, and biological sciences
Abstract
12.1 Introduction
12.2 Machine translation
12.3 Robotics
12.4 Biological sciences
12.5 Summary
Chapter 13: Application II: Medical and health-care informatics, economics, business, and finance
Abstract
13.1 Introduction
13.2 Healthcare
13.3 Economics
13.4 Business and finance
13.5 Summary
13.6 Postscript: Psychology
Chapter 14: Discussion, conclusions, and the future of data
Abstract
14.1 Chapter 1
14.2 Chapter 2
14.3 Chapter 3
14.4 Chapter 4
14.5 Chapter 5
14.6 Chapter 6
14.7 Chapter 7
14.8 Chapter 8
14.9 Chapter 9
14.10 Chapter 10
14.11 Chapter 11
14.12 Chapter 12
14.13 Chapter 13
14.14 The future of meta-analytics
Index
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Table of Contents
Meta-Analytics
Consensus Approaches and System Patterns for Data Analysis
First Edition
Steven Simske
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