Meta-Analytics
Consensus Approaches and System Patterns for Data Analysis
Published on 13. March 2019
Book
Paperback/Softback
340 pages
978-0-12-814623-1 (ISBN)
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.
Inn addition, the analytics within can be applied to predictive algorithms for everyone from police departments to sports analysts.
More details
Language
English
Place of publication
San Francisco
United States
Publishing group
Elsevier Science & Technology
Target group
Professional and scholarly
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.
Product notice
Paperback (trade)
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 18 mm
Weight
590 gr
ISBN-13
978-0-12-814623-1 (9780128146231)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
E-Book
03/2019
Morgan Kaufmann
€60.95
Available for download
Person
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.
Content
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