| Description |
1 online resource |
| Note |
Includes index. |
| Bibliography |
Includes bibliographical references and index. |
| Contents |
Ground truthing -- Experiment design -- Meta-Analytic design patterns -- Sensitivity analysis and big system engineering -- Multi-path predictive selection -- Modeling and model fitting: including Antibody model, stem-differentiated cell model, and chemical, physical and environmental models for greater diversity in form -- Synonym-antonym and Reinforce-Void patterns and their value in data consensus, data anonymization, and data normalization -- Meta-analytics as analytics around analytics (functional metrics, entropy, EM). Ingesting statistical approaches for specific domains and generalizing them for data hybrid systems -- System design optimization (entropy, error variance, coupling minimization F-score) -- Aleatory techniques/expert system techniques ... tie to ground truthing and error testing -- Applications: machine translation, robotics, biological and social sciences, medical and healthcare informatics, economics, business and finance -- Discussion and Conclusions, and the Future of Data. |
| Summary |
We live in a world in which huge volumes of data are being collected. The machine intelligence community has been very successful in turning this data into information. Taking the power of hybrid architectures as a starting point, analytics approaches can be upgraded. Meta-Analytics supplies 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 behaviour than the use of traditional analytics approaches alone. The book is 'meta' to analytics, and so covers general analytics in sufficient detail for the reader to engage with and understand hybrid or meta- approaches. It allows a relative novice to quickly achieve high-level competency. The title has relevance to machine translation, robotics, biological and social sciences, medical and healthcare informatics, economics, business and finance. The analytics can be applied to predictive algorithms for everyone from police departments to sports analysts -- Provided by publisher. |
| Subject |
Data mining.
|
|
Machine learning.
|
|
Data Mining |
|
Machine Learning |
|
Exploration de données (Informatique) |
|
Apprentissage automatique. |
|
Data mining |
|
Machine learning |
| Other Form: |
Print version: Simske, Steven J. Meta-analytics. Cambridge, MA : Morgan Kaufmann, an imprint of Elsevier, 2019 0128146230 9780128146231 (OCoLC)1077575519 |
| ISBN |
9780128146248 (electronic bk.) |
|
0128146249 (electronic bk.) |
|
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