| 書(shū)目名稱 | Machine Learning |
| 副標(biāo)題 | Modeling Data Locall |
| 編輯 | Kaizhu Huang,Haiqin Yang,Michael Lyu |
| 視頻video | http://file.papertrans.cn/621/620373/620373.mp4 |
| 概述 | New unified theory.Detailed graphic illustration.Empirical validation for each model |
| 叢書(shū)名稱 | Advanced Topics in Science and Technology in China |
| 圖書(shū)封面 |  |
| 描述 | .Machine Learning - Modeling Data Locally and Globally presents a novel and unified theory that tries to seamlessly integrate different algorithms. Specifically, the book distinguishes the inner nature of machine learning algorithms as either "local learning"or "global learning."This theory not only connects previous machine learning methods, or serves as roadmap in various models, but – more importantly – it also motivates a theory that can learn from data both locally and globally. This would help the researchers gain a deeper insight and comprehensive understanding of the techniques in this field. The book reviews current topics,new theories and applications...Kaizhu Huang was a researcher at the Fujitsu Research and Development Center and is currently a research fellow in the Chinese University of Hong Kong. Haiqin Yang leads the image processing group at HiSilicon Technologies. Irwin King and Michael R. Lyu are professors at the Computer Science and Engineering departmentof the Chinese University of Hong Kong.. |
| 出版日期 | Book 2008 |
| 關(guān)鍵詞 | ATSTC; Global learning; Hybrid learning; Kernelization; Local learning; ZJUP; algorithms; computer science; |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-540-79452-3 |
| isbn_ebook | 978-3-540-79452-3Series ISSN 1995-6819 Series E-ISSN 1995-6827 |
| issn_series | 1995-6819 |
| copyright | Springer-Verlag Berlin Heidelberg 2008 |
|Archiver|手機(jī)版|小黑屋|
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