| 書目名稱 | Principles and Theory for Data Mining and Machine Learning |
| 編輯 | Bertrand Clarke,Ernest Fokoue,Hao Helen Zhang |
| 視頻video | http://file.papertrans.cn/756/755434/755434.mp4 |
| 概述 | This is a more theoretical book on the same subject as the book on statistical learning by Hastie/Tibshirani/Friedman..Request lecturer material: |
| 叢書名稱 | Springer Series in Statistics |
| 圖書封面 |  |
| 描述 | The idea for this book came from the time the authors spent at the Statistics and Applied Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina starting in fall 2003. The rst author was there for a total of two years, the rst year as a Duke/SAMSI Research Fellow. The second author was there for a year as a Post-Doctoral Scholar. The third author has the great fortune to be in RTP p- manently. SAMSI was – and remains – an incredibly rich intellectual environment with a general atmosphere of free-wheeling inquiry that cuts across established elds. SAMSI encourages creativity: It is the kind of place where researchers can be found at work in the small hours of the morning – computing, interpreting computations, and developing methodology. Visiting SAMSI is a unique and wonderful experience. The people most responsible for making SAMSI the great success it is include Jim Berger, Alan Karr, and Steve Marron. We would also like to express our gratitude to Dalene Stangl and all the others from Duke, UNC-Chapel Hill, and NC State, as well as to the visitors (short and long term) who were involved in the SAMSI programs. It was a magical time we remember with on |
| 出版日期 | Book 2009 |
| 關(guān)鍵詞 | Clustering; classification; data mining; linear regression; machine learning; supervised learning; unsuper |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-0-387-98135-2 |
| isbn_softcover | 978-1-4614-1707-1 |
| isbn_ebook | 978-0-387-98135-2Series ISSN 0172-7397 Series E-ISSN 2197-568X |
| issn_series | 0172-7397 |
| copyright | Springer-Verlag New York 2009 |
|Archiver|手機(jī)版|小黑屋|
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