| 书目名称 | Kernel Ridge Regression in Clinical Research | | 编辑 | Ton J. Cleophas,Aeilko H. Zwinderman | | 视频video | | | 概述 | A virtually unpublished statistical analysis method‘for pattern recognition in high dimensional data.A complete comparison against traditional methods shows that the latter is uniformly outperformed b | | 图书封面 |  | | 描述 | .IBM (international business machines) has published in its SPSS statistical software 2022 update a very important novel regression method entitled Kernel Ridge Regression (KRR). It is an extension of the currently available regression methods, and is suitable for pattern recognition in high dimensional data, particularly, when alternative methods fail. Its theoretical advantages are plenty and include the.kernel trick for reduced arithmetic complexity,.estimation of uncertainty by Gaussians unlike histograms,.corrected data-overfit by ridge regularization,.availability of 8 alternative kernel density models for datafit..A very exciting and wide array of preliminary KRR research has already been published by major disciplines (like studies in quantum mechanics and nuclear physics, studies of molecular affinity / dynamics, atomisation energy studies, but also forecasting economics studies, IoT (internet of things)studies for e-networks, plant stress response studies, big data streaming studies, etc). In contrast, it is virtually unused in clinical research. This edition is the first textbook and tutorial of kernel ridge regressions for medical and healthcare students as well as reco | | 出版日期 | Textbook 2022 | | 关键词 | Kernel Ridge Regression; Statistical Data Analysis; Clinical Medicine; Step by Step Analyses for Self-a | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-10717-7 | | isbn_softcover | 978-3-031-10719-1 | | isbn_ebook | 978-3-031-10717-7 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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发表于 2025-3-21 17:45:21
