| 書(shū)目名稱(chēng) | Machine Learning | | 副標(biāo)題 | Discriminative and G | | 編輯 | Tony Jebara | | 視頻video | http://file.papertrans.cn/621/620372/620372.mp4 | | 叢書(shū)名稱(chēng) | The Springer International Series in Engineering and Computer Science | | 圖書(shū)封面 |  | | 描述 | .Machine Learning:. .Discriminative and Generative. covers the main contemporary themes and tools in machine learning ranging from Bayesian probabilistic models to discriminative support-vector machines. However, unlike previous books that only discuss these rather different approaches in isolation, it bridges the two schools of thought together within a common framework, elegantly connecting their various theories and making one common big-picture. Also, this bridge brings forth new hybrid discriminative-generative tools that combine the strengths of both camps. This book serves multiple purposes as well. The framework acts as a scientific breakthrough, fusing the areas of generative and discriminative learning and will be of interest to many researchers. However, as a conceptual breakthrough, this common framework unifies many previously unrelated tools and techniques and makes them understandable to a larger portion of the public. This gives the more practical-minded engineer, student and the industrial public an easy-access and more sensible road map into the world of machine learning. ..Machine Learning: Discriminative and Generative. is designed for an audience composed of re | | 出版日期 | Book 2004 | | 關(guān)鍵詞 | Extension; computer science; learning; machine learning | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4419-9011-2 | | isbn_softcover | 978-1-4613-4756-9 | | isbn_ebook | 978-1-4419-9011-2Series ISSN 0893-3405 | | issn_series | 0893-3405 | | copyright | Springer Science+Business Media New York 2004 |
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