| 书目名称 | Machine Learning |
| 副标题 | Modeling Data Locall |
| 编辑 | Kaizhu Huang,Haiqin Yang,Michael Lyu |
| 视频video | |
| 概述 | New unified theory.Detailed graphic illustration.Empirical validation for each model |
| 丛书名称 | Advanced Topics in Science and Technology in China |
| 图书封面 |  |
| 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 |
| 关键词 | 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 |
发表于 2025-3-21 17:13:25
