Meta-Analytics
Consensus Approaches and System Patterns for Data Analysis
Description
Key Features
- Provides comprehensive and systematic coverage of machine learning-based data analysis tasks
- Enables rapid progress towards competency in data analysis techniques
- Gives exhaustive and widely applicable patterns for use by data scientists
- Covers hybrid or ‘meta’ approaches, along with general analytics
- Lays out information and practical guidance on data analysis for practitioners working across all sectors
Readership
Data scientists in all sectors: academia, industry, government and NGO; engineering students, computer science students, engineers; computer scientists, researchers, analytics engineers, intelligent system designers, data mining professionals, robust learning system professionals of all job descriptions
Table of Contents
1. Ground truthing
2. Experiment design
3. Meta-Analytic design patterns
4. Sensitivity analysis and big system engineering
5. Multi-path predictive selection
6. Modeling and model fitting: including Antibody model, stem-differentiated cell model, and chemical, physical and environmental models for greater diversity in form
7. Synonym-antonym and Reinforce-Void patterns and their value in data consensus, data anonymization, and data normalization
8. Meta-analytics as analytics around analytics (functional metrics, entropy, EM). Ingesting statistical approaches for specific domains and generalizing them for data hybrid systems
9. System design optimization (entropy, error variance, coupling minimization F-score)
10. Aleatory techniques/expert system techniques…tie to ground truthing and error testing
11. Applications: machine translation, robotics, biological and social sciences, medical and healthcare informatics, economics, business and finance
12. Discussion and Conclusions, and the Future of Data
Product details
- No. of pages: 340
- Language: English
- Copyright: © Morgan Kaufmann 2019
- Published: March 10, 2019
- Imprint: Morgan Kaufmann
- Paperback ISBN: 9780128146231
- eBook ISBN: 9780128146248