01393aam 2200373I 450 991070956000332120151026040613.0GOVPUB-C13-1670fcd0924a134b5e633649c3a760e5(CKB)5470000002479400(OCoLC)926748982(EXLCZ)99547000000247940020151026d1988 ua 0engrdacontentrdamediardacarrierData bases available at the National Institute of Standards and Technology Research Information Center /Diane CunninghamGaithersburg, MD :U.S. Dept. of Commerce, National Institute of Standards and Technology,1988.1 online resourceNIST special publication ;7531988.Contributed record: Metadata reviewed, not verified. Some fields updated by batch processes.Title from PDF title page.Includes bibliographical references.Cunningham Diane1388852Cunningham Diane1388852National Institute of Standards and Technology (U.S.)NBSNBSGPOBOOK9910709560003321Data bases available at the National Institute of Standards and Technology Research Information Center3547484UNINA03139nam 22004935 450 991029957710332120251113183101.03-319-73040-110.1007/978-3-319-73040-0(CKB)4100000001794713(DE-He213)978-3-319-73040-0(MiAaPQ)EBC5231082(PPN)223956317(EXLCZ)99410000000179471320180118d2018 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierVisual Knowledge Discovery and Machine Learning /by Boris Kovalerchuk1st ed. 2018.Cham :Springer International Publishing :Imprint: Springer,2018.1 online resource (XXI, 317 p. 274 illus., 263 illus. in color.) Intelligent Systems Reference Library,1868-4408 ;1443-319-73039-8 Includes bibliographical references at the end of each chapters and index.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.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.Intelligent Systems Reference Library,1868-4408 ;144Computational intelligenceArtificial intelligenceComputational IntelligenceArtificial IntelligenceComputational intelligence.Artificial intelligence.Computational Intelligence.Artificial Intelligence.006.3Kovalerchuk Borisauthttp://id.loc.gov/vocabulary/relators/aut846538BOOK9910299577103321Visual Knowledge Discovery and Machine Learning2542748UNINA