Visual Knowledge Discovery and Machine Learning

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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

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