Boris Kovalerchuk

Visual Knowledge Discovery and Machine Learning (eBook, PDF)

• Format: PDF

Produktbeschreibung

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 hyper-tubes, hyper-planes, hyper-spheres, etc. using lossless visual data representations

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 317
• Erscheinungstermin: 17. Januar 2018
• Englisch
• ISBN-13: 9783319730400
• Artikelnr.: 52938229

Inhaltsangabe

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.

Rezensionen

"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)