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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.
- 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
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
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
- No. of pages:
- © Morgan Kaufmann 2019
- 10th March 2019
- Morgan Kaufmann
- Paperback ISBN:
- eBook ISBN:
Steven J Simske is HP Fellow and Director at Hewlett Packard Labs, and has worked in machine intelligence and analytics for the past 25 years, with domains extending from medical image analytics to text summarization. He has performed research relevant to meta analytics for over 20 years at HP Labs, and in collaboration with major universities in the US and Brazil.
HP Fellow and Director, HP Labs, HP Inc, CO, USA
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