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Inductive learning algorithms and representations for text categorization
Abstract
Text categorization – the assignment of natural language texts to one or more predefined categories based on their content – is an important component in many information organization and management tasks. We compare the effectiveness of five different automatic learning algorithms for text categorization in terms of learning speed, realtime classification speed, and classification accuracy. We also examine training set size, and alternative document representations. Very accurate text classifiers can be learned automatically from training examples. Linear Support Vector Machines (SVMs) are particularly promising because they are very accurate, quick to train, and quick to evaluate. 1.1 Keywords Text categorization, classification, support vector machines, machine learning, information management.
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The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
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Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., & Scholkopf, B. (1998) · IEEE Intelligent Systems and their Applications · doi:10.1109/5254.708428 · Cited by 7,243
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Metadata
- Authors
- Dumais, S., Platt, J., Heckerman, D., & Sahami, M.
- Venue
- Proceedings of the seventh international conference on Information and knowledge management
- Publisher
- ACM
- Published
- 1998-11
- Pages
- 148-155
- OpenAlex
- W2170654002
- Cited by
- 1,461
- References
- 2
- FWCI
- 32.09
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Dumais, S., Platt, J., Heckerman, D., & Sahami, M. (1998). Inductive learning algorithms and representations for text categorization. Proceedings of the seventh international conference on Information and knowledge management. https://doi.org/10.1145/288627.288651
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