| 书目名称 | Visual Knowledge Discovery and Machine Learning | | 编辑 | Boris Kovalerchuk | | 视频video | | | 概述 | 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 | | 丛书名称 | Intelligent Systems Reference Library | | 图书封面 |  | | 描述 | .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.. | | 出版日期 | Book 2018 | | 关键词 | Intelligent Systems; Data Science; Knowledge Discovery; Visual Data Mining; Machine Learning; Multidimens | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-319-73040-0 | | isbn_softcover | 978-3-319-89230-6 | | isbn_ebook | 978-3-319-73040-0Series ISSN 1868-4394 Series E-ISSN 1868-4408 | | issn_series | 1868-4394 | | copyright | Springer International Publishing AG 2018 |
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发表于 2025-3-21 16:59:05
