| 書目名稱 | Machine Learning in Medicine – A Complete Overview | | 編輯 | Ton J. Cleophas,Aeilko H. Zwinderman | | 視頻video | http://file.papertrans.cn/621/620697/620697.mp4 | | 概述 | Improved version of the best-seller in 2015 removing textual errors, including original tables from the software programs as applied, providing full color graphs.This edition each chapter is extended | | 圖書封面 |  | | 描述 | .Adequate health and health care is no longer possible without proper data supervision from modern machine learning methodologies like cluster models, neural networks, and other data mining methodologies. The current book is the first publication of a complete overview of machine learning methodologies for the medical and health sector, and it was written as a training companion, and as a must-read, not only for physicians and students, but also for any one involved in the process and progress of health and health care..In this second edition the authors have removed the textual errors from the first?edition. Also, the improved tables from the first edition, have been replaced with the??original tables from the software programs as applied. This is, because, unlike the former, the latter were without error, and readers were better familiar with them..The main purpose of the first edition was, to provide stepwise analyses of the?novel??methods from data examples, but background information and clinical?relevance information may have been somewhat lacking. Therefore, each?chapter now contains a section entitled "Background Information"..Machine learning may be more informative, and m | | 出版日期 | Textbook 2020Latest edition | | 關(guān)鍵詞 | Coputer science; Data mining; Machine learning; SPSS statistical software; various data mining software | | 版次 | 2 | | doi | https://doi.org/10.1007/978-3-030-33970-8 | | isbn_softcover | 978-3-030-33972-2 | | isbn_ebook | 978-3-030-33970-8 | | copyright | Springer Nature Switzerland AG 2020 |
The information of publication is updating
|
|
|Archiver|手機(jī)版|小黑屋|
派博傳思國(guó)際
( 京公網(wǎng)安備110108008328)
GMT+8, 2025-10-14 19:40
發(fā)表于 2025-3-21 17:46:52
