Titlebook: Visual Knowledge Discovery and Machine Learning; Boris Kovalerchuk Book 2018 Springer International Publishing AG 2018 Intelligent Systems

寬大

Motivation, Problems and Approach,eversible lossy visual representations of n-D data along with their impact on efficiency of solving Data Mining/Machine Learning tasks. The approach concentrates on reversible representations along with the hybrid methodology to mitigate deficiencies of different representations?.

Observe

SEED

Discovering Visual Features and Shape Perception Capabilities in GLC,s for classification these high-dimensional data. The chapter concludes with a description of the cooperative visualization approach to enhance Knowledge Discovery in solving Data Mining/Machine Learning tasks.?

gusher

Pareto Front and General Line Coordinates,pter shows a way to accomplish this with GLC-L visualization method defined Chap. 7. It also shows a way to visualize the approximation set for the Pareto Front with Collocated Paired Coordinates, defined in Chap. 2 in comparison with Parallel Coordinates to assists in finding “best” Pareto points.

definition

Toward Virtual Data Scientist and Super-Intelligence with Visual Means, to meet this Big data challenge, with a minimal contribution from data scientists. This chapter describes our vision of such a “virtual data scientist”, based on the visual approach of the General Line Coordinates.

DAUNT

附錄

acrophobia

Interactive Visual Classification, Clustering and Dimension Reduction with GLC-L,relations and dimension reduction. Classification and dimension reduction tasks from three domains, image processing, computer-aided medical diagnostics and finance (stock market), are used to illustrate this method.

可行

zonules

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