Learning to Classify Text Using Support Vector Machines:
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 particula...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
2002
|
| Schriftenreihe: | The Springer International Series in Engineering and Computer Science
668 |
| Schlagworte: | |
| Online-Zugang: | FHI01 BTU01 URL des Erstveröffentlichers |
| Zusammenfassung: | 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 |
| Beschreibung: | 1 Online-Ressource (XVII, 205 p) |
| ISBN: | 9781461509073 |
| DOI: | 10.1007/978-1-4615-0907-3 |
Internformat
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| 520 | |a 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 | ||
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| spelling | Joachims, Thorsten Verfasser aut Learning to Classify Text Using Support Vector Machines by Thorsten Joachims Boston, MA Springer US 2002 1 Online-Ressource (XVII, 205 p) txt rdacontent c rdamedia cr rdacarrier The Springer International Series in Engineering and Computer Science 668 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 Computer Science Artificial Intelligence (incl. Robotics) Information Storage and Retrieval Data Structures, Cryptology and Information Theory Information Systems Applications (incl. Internet) Computer science Data structures (Computer science) Information storage and retrieval Artificial intelligence Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Textverarbeitung Linguistik (DE-588)4259878-3 gnd rswk-swf 1\p (DE-588)4113937-9 Hochschulschrift gnd-content Textverarbeitung Linguistik (DE-588)4259878-3 s Maschinelles Lernen (DE-588)4193754-5 s 2\p DE-604 Erscheint auch als Druck-Ausgabe 9781461352983 https://doi.org/10.1007/978-1-4615-0907-3 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Joachims, Thorsten Learning to Classify Text Using Support Vector Machines Computer Science Artificial Intelligence (incl. Robotics) Information Storage and Retrieval Data Structures, Cryptology and Information Theory Information Systems Applications (incl. Internet) Computer science Data structures (Computer science) Information storage and retrieval Artificial intelligence Maschinelles Lernen (DE-588)4193754-5 gnd Textverarbeitung Linguistik (DE-588)4259878-3 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)4259878-3 (DE-588)4113937-9 |
| title | Learning to Classify Text Using Support Vector Machines |
| title_auth | Learning to Classify Text Using Support Vector Machines |
| title_exact_search | Learning to Classify Text Using Support Vector Machines |
| title_full | Learning to Classify Text Using Support Vector Machines by Thorsten Joachims |
| title_fullStr | Learning to Classify Text Using Support Vector Machines by Thorsten Joachims |
| title_full_unstemmed | Learning to Classify Text Using Support Vector Machines by Thorsten Joachims |
| title_short | Learning to Classify Text Using Support Vector Machines |
| title_sort | learning to classify text using support vector machines |
| topic | Computer Science Artificial Intelligence (incl. Robotics) Information Storage and Retrieval Data Structures, Cryptology and Information Theory Information Systems Applications (incl. Internet) Computer science Data structures (Computer science) Information storage and retrieval Artificial intelligence Maschinelles Lernen (DE-588)4193754-5 gnd Textverarbeitung Linguistik (DE-588)4259878-3 gnd |
| topic_facet | Computer Science Artificial Intelligence (incl. Robotics) Information Storage and Retrieval Data Structures, Cryptology and Information Theory Information Systems Applications (incl. Internet) Computer science Data structures (Computer science) Information storage and retrieval Artificial intelligence Maschinelles Lernen Textverarbeitung Linguistik Hochschulschrift |
| url | https://doi.org/10.1007/978-1-4615-0907-3 |
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