Visual Knowledge Discovery and Machine Learning
(Sprache: Englisch)
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...
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Klappentext zu „Visual Knowledge Discovery and Machine Learning “
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.Inhaltsverzeichnis zu „Visual Knowledge Discovery and Machine Learning “
Motivation, Problems and Approach.- General Line Coordinates (GLC).- Theoretical and Mathematical Basis of GLC.- Adjustable GLCs for decreasing occlusion and pattern simplification.- GLC Case Studies.- Discovering visual features and shape perception capabilities in GLC.- Interactive Visual Classification, Clustering and Dimension Reduction with GLC-L.- Knowledge Discovery and Machine Learning for Investment Strategy with CPC.Bibliographische Angaben
- Autor: Boris Kovalerchuk
- 2019, Softcover reprint of the original 1st ed. 2018, XXI, 317 Seiten, 263 farbige Abbildungen, Maße: 15,6 x 23,6 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 3319892304
- ISBN-13: 9783319892306
Sprache:
Englisch
Pressezitat
"The book is a good suggestion for a data scientist or someone who would like to specialise on GLCs ... it provides a helpful introduction along with a wide variety of case studies that help any scientist to familiarise with this method." (Angeliki Katsenou, Perception, Vol. 47 (12), December, 2018)
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