Visual Knowledge Discovery and Machine Learning (Hardcover, 1st ed. 2018)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Intelligent Systems Reference Library, 144
Release date:	2018
First published:	2018
Authors:	Boris Kovalerchuk
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	317
Edition:	1st ed. 2018
ISBN-13:	978-3-319-73039-4
Categories:	Books > Computing & IT > General theory of computing > Data structures Books > Computing & IT > Computer programming > Algorithms & procedures Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	3-319-73039-8
Barcode:	9783319730394

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