| 书目名称 | Learning to Classify Text Using Support Vector Machines | | 编辑 | Thorsten Joachims | | 视频video | | | 丛书名称 | The Springer International Series in Engineering and Computer Science | | 图书封面 |  | | 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 | | 关键词 | Support Vector Machine; algorithms; classification; cognition; computer science; information; learning; lea | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4615-0907-3 | | isbn_softcover | 978-1-4613-5298-3 | | isbn_ebook | 978-1-4615-0907-3Series ISSN 0893-3405 | | issn_series | 0893-3405 | | copyright | Springer Science+Business Media New York 2002 |
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发表于 2025-3-21 17:00:59
