Optimizing Methods in Statistics
Author: Jagdish S. Rustagi
Publisher:
Published: 1979
Total Pages: 582
ISBN-13:
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Author: Jagdish S. Rustagi
Publisher:
Published: 1979
Total Pages: 582
ISBN-13:
DOWNLOAD EBOOKAuthor: 国立国会図書館 (Japan)
Publisher:
Published: 1972
Total Pages: 672
ISBN-13:
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Publisher:
Published: 1974
Total Pages: 520
ISBN-13:
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Publisher:
Published: 1981
Total Pages: 736
ISBN-13:
DOWNLOAD EBOOKAuthor: Hanan Samet
Publisher: Morgan Kaufmann
Published: 2006-08-08
Total Pages: 1023
ISBN-13: 0123694469
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Author: Helen M. Wood
Publisher:
Published: 1976
Total Pages: 184
ISBN-13:
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Publisher:
Published: 1974
Total Pages: 650
ISBN-13:
DOWNLOAD EBOOKAuthor: Chis, Monica
Publisher: IGI Global
Published: 2010-06-30
Total Pages: 282
ISBN-13: 1615208100
DOWNLOAD EBOOKEvolutionary Computation and Optimization Algorithms in Software Engineering: Applications and Techniques lays the foundation for the successful integration of evolutionary computation into software engineering. It surveys techniques ranging from genetic algorithms, to swarm optimization theory, to ant colony optimization, demonstrating their uses and capabilities. These techniques are applied to aspects of software engineering such as software testing, quality assessment, reliability assessment, and fault prediction models, among others, to providing researchers, scholars and students with the knowledge needed to expand this burgeoning application.
Author: Francesco Archetti
Publisher: Springer Nature
Published: 2019-09-25
Total Pages: 137
ISBN-13: 3030244946
DOWNLOAD EBOOKThis volume brings together the main results in the field of Bayesian Optimization (BO), focusing on the last ten years and showing how, on the basic framework, new methods have been specialized to solve emerging problems from machine learning, artificial intelligence, and system optimization. It also analyzes the software resources available for BO and a few selected application areas. Some areas for which new results are shown include constrained optimization, safe optimization, and applied mathematics, specifically BO's use in solving difficult nonlinear mixed integer problems. The book will help bring readers to a full understanding of the basic Bayesian Optimization framework and gain an appreciation of its potential for emerging application areas. It will be of particular interest to the data science, computer science, optimization, and engineering communities.