Visual Knowledge Discovery and Machine Learning

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, General Computing
Cover of the book Visual Knowledge Discovery and Machine Learning by Boris Kovalerchuk, Springer International Publishing
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Author: Boris Kovalerchuk ISBN: 9783319730400
Publisher: Springer International Publishing Publication: January 17, 2018
Imprint: Springer Language: English
Author: Boris Kovalerchuk
ISBN: 9783319730400
Publisher: Springer International Publishing
Publication: January 17, 2018
Imprint: Springer
Language: English

This book combines the advantages of high-dimensional data visualization and machine learning in the context of identifying complex n-D data patterns. It vastly expands the class of reversible lossless 2-D and 3-D visualization methods, which preserve the n-D information. This class of visual representations, called the General Lines Coordinates (GLCs), is accompanied by a set of algorithms for n-D data classification, clustering, dimension reduction, and Pareto optimization. The mathematical and theoretical analyses and methodology of GLC are included, and the usefulness of this new approach is demonstrated in multiple case studies. These include the Challenger disaster, world hunger data, health monitoring, image processing, text classification, market forecasts for a currency exchange rate, computer-aided medical diagnostics, and others. As such, the book offers a unique resource for students, researchers, and practitioners in the emerging field of Data Science.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This book combines the advantages of high-dimensional data visualization and machine learning in the context of identifying complex n-D data patterns. It vastly expands the class of reversible lossless 2-D and 3-D visualization methods, which preserve the n-D information. This class of visual representations, called the General Lines Coordinates (GLCs), is accompanied by a set of algorithms for n-D data classification, clustering, dimension reduction, and Pareto optimization. The mathematical and theoretical analyses and methodology of GLC are included, and the usefulness of this new approach is demonstrated in multiple case studies. These include the Challenger disaster, world hunger data, health monitoring, image processing, text classification, market forecasts for a currency exchange rate, computer-aided medical diagnostics, and others. As such, the book offers a unique resource for students, researchers, and practitioners in the emerging field of Data Science.

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