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Author:   Steven Simske (HP Fellow and Director, HP Labs, HP Inc, CO, USA)
Publisher:   Elsevier Science & Technology
Imprint:   Morgan Kaufmann Publishers In
Weight:   0.700kg
ISBN:  

9780128146231

ISBN 10:   0128146230
Pages:   340
Publication Date:   13 March 2019
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
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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

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Author Information

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.

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