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Visual Knowledge Discovery and Machine Learning
Details
Expands methods of knowledge discovery based on visual means
Generates new lossless visual representations of n-D data in 2-D that fully preserves n-D data with focus on Machine Learning/ Data Mining goals, in contrast with a generic visualization without a clearly specified goal
Provides clear interpretation of features of visual representations in terms of n-D data properties
Effectively usrees human vision capabilities of shape perception in mapping n-D data points into 2-D graphs
Recognizes n-D data structures such as hypertubes, hyper-planes, hyper-spheres, etc. using lossless visual data representations
Expands methods of knowledge discovery based on visual means Generates new lossless visual representations of n-D data in 2-D that fully preserve n-D data with a focus on machine learning/data mining goals, in contrast to a generic visualization without a clearly specified goal Effectively uses human shape perception capabilities in mapping n-D data points into 2-D graphs Identifies n-D data structures such as hyper-tubes, hyperplanes, hyper-spheres, etc. using lossless visual data representations
Zusammenfassung
Expands methods of knowledge discovery based on visual means
Generates new lossless visual representations of n-D data in 2-D that fully preserves n-D data with focus on Machine Learning/ Data Mining goals, in contrast with a generic visualization without a clearly specified goal
Provides clear interpretation of features of visual representations in terms of n-D data properties
Effectively usrees human vision capabilities of shape perception in mapping n-D data points into 2-D graphs
Recognizes n-D data structures such as hypertubes, hyper-planes, hyper-spheres, etc. using lossless visual data representations
Inhalt
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.
Weitere Informationen
- Allgemeine Informationen
- GTIN 09783319892306
- Auflage Softcover reprint of the original 1st edition 2018
- Sprache Englisch
- Genre Allgemeines & Lexika
- Lesemotiv Verstehen
- Größe H235mm x B155mm x T19mm
- Jahr 2019
- EAN 9783319892306
- Format Kartonierter Einband
- ISBN 3319892304
- Veröffentlichung 04.06.2019
- Titel Visual Knowledge Discovery and Machine Learning
- Autor Boris Kovalerchuk
- Untertitel Intelligent Systems Reference Library 144
- Gewicht 517g
- Herausgeber Springer
- Anzahl Seiten 340