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Titlebook: Learning to Classify Text Using Support Vector Machines; Thorsten Joachims Book 2002 Springer Science+Business Media New York 2002 Support

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發(fā)表于 2025-3-21 17:00:59 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Learning to Classify Text Using Support Vector Machines
編輯Thorsten Joachims
視頻videohttp://file.papertrans.cn/583/582991/582991.mp4
叢書名稱The Springer International Series in Engineering and Computer Science
圖書封面Titlebook: Learning to Classify Text Using Support Vector Machines;  Thorsten Joachims Book 2002 Springer Science+Business Media New York 2002 Support
描述.Based on ideas from Support Vector Machines (SVMs), .Learning To Classify Text Using Support Vector Machines. presents a new approach to generating text classifiers from examples. The approach combines high performance and efficiency with theoretical understanding and improved robustness. In particular, it is highly effective without greedy heuristic components. The SVM approach is computationally efficient in training and classification, and it comes with a learning theory that can guide real-world applications...Learning To Classify Text Using Support Vector Machines. gives a complete and detailed description of the SVM approach to learning text classifiers, including training algorithms, transductive text classification, efficient performance estimation, and a statistical learning model of text classification. In addition, it includes an overview of the field of text classification, making it self-contained even for newcomers to the field. This book gives a concise introduction to SVMs for pattern recognition, and it includes a detailed description of how to formulate text-classification tasks for machine learning..
出版日期Book 2002
關(guān)鍵詞Support Vector Machine; algorithms; classification; cognition; computer science; information; learning; lea
版次1
doihttps://doi.org/10.1007/978-1-4615-0907-3
isbn_softcover978-1-4613-5298-3
isbn_ebook978-1-4615-0907-3Series ISSN 0893-3405
issn_series 0893-3405
copyrightSpringer Science+Business Media New York 2002
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Thorsten Joachims of the data is that the entire operating region of the system is covered, i.e. no special calibration cycles are required. Two truck engine applications are evaluated, one where a 1-D air mass-flow sensor adaptation map is estimated, and one where a 2-D volumetric efficiency map is adapted, both du
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Thorsten Joachims of the data is that the entire operating region of the system is covered, i.e. no special calibration cycles are required. Two truck engine applications are evaluated, one where a 1-D air mass-flow sensor adaptation map is estimated, and one where a 2-D volumetric efficiency map is adapted, both du
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Thorsten Joachims of the data is that the entire operating region of the system is covered, i.e. no special calibration cycles are required. Two truck engine applications are evaluated, one where a 1-D air mass-flow sensor adaptation map is estimated, and one where a 2-D volumetric efficiency map is adapted, both du
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發(fā)表于 2025-3-23 00:23:11 | 只看該作者
Thorsten Joachimsl variables were available, or, from a Bayesian approach, if informative prior distrubutions for the parameters were used (see Johnston [1965, chap. 6] and Zellner [1971, chap. V]).. None of this prior information seemed very appealing to econometricians.
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