{"product_id":"learning-to-classify-text-using-support-vector-machines-hardcover","title":"Learning to Classify Text Using Support Vector Machines - Hardcover","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eThorsten Joachims\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eText Classification, or the task of automatically assigning semantic categories to natural language text, has become one of the key methods for organizing online information. Since hand-coding classification rules is costly or even impractical, most modern approaches employ machine learning techniques to automatically learn text classifiers from examples. However, none of these conventional approaches combines good prediction performance, theoretical understanding, and efficient training algorithms.\u003c\/p\u003e \u003cp\u003eBased on ideas from Support Vector Machines (SVMs), \u003cstrong\u003eLearning To Classify Text Using Support Vector Machines\u003c\/strong\u003e 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.\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eLearning To Classify Text Using Support Vector Machines\u003c\/strong\u003e 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.\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eLearning To Classify Text Using Support Vector Machines\u003c\/strong\u003e is designed as a reference for researchers and practitioners, and is suitable as a secondary text for graduate-level students in Computer Science within Machine Learning and Language Technology. \u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 205\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.8 x 9.54 x 6.46 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e April 30, 2002\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":47638589014275,"sku":"9780792376798","price":6258.43,"currency_code":"TWD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0588\/9310\/7359\/files\/bmFQWXk2eXNubFp6ckZiOUFnaUVxUT09.webp?v=1768614709","url":"https:\/\/annizon.com\/en-tw\/products\/learning-to-classify-text-using-support-vector-machines-hardcover","provider":"annizon.com","version":"1.0","type":"link"}