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Full Description
Meta-Analytics: Consensus Approaches and System Patterns for Data Analysis presents an exhaustive set of patterns for data science to use on any machine learning based data analysis task. The book virtually ensures that at least one pattern will lead to better overall system behavior than the use of traditional analytics approaches. The book is 'meta' to analytics, covering general analytics in sufficient detail for readers to engage with, and understand, hybrid or meta- approaches. The book has relevance to machine translation, robotics, biological and social sciences, medical and healthcare informatics, economics, business and finance.
Inn addition, the analytics within can be applied to predictive algorithms for everyone from police departments to sports analysts.
Contents
1. Ground truthing2. Experiment design3. Meta-Analytic design patterns4. Sensitivity analysis and big system engineering5. Multi-path predictive selection6. Modeling and model fitting: including Antibody model, stem-differentiated cell model, and chemical, physical and environmental models for greater diversity in form7. Synonym-antonym and Reinforce-Void patterns and their value in data consensus, data anonymization, and data normalization8. Meta-analytics as analytics around analytics (functional metrics, entropy, EM). Ingesting statistical approaches for specific domains and generalizing them for data hybrid systems9. System design optimization (entropy, error variance, coupling minimization F-score)10. Aleatory techniques/expert system techniques...tie to ground truthing and error testing11. Applications: machine translation, robotics, biological and social sciences, medical and healthcare informatics, economics, business and finance12. Discussion and Conclusions, and the Future of Data
