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A statistical learning learning model of text classification for support vector machines
Abstract
This paper develops a theoretical learning model of text classification for Support Vector Machines (SVMs). It connects the statistical properties of text-classification tasks with the generalization performance of a SVM in a quantitative way. Unlike conventional approaches to learning text classifiers, which rely primarily on empirical evidence, this model explains why and when SVMs perform well for text classification. In particular, it addresses the following questions: Why can support vector machines handle the large feature spaces in text classification effectively? How is this related to the statistical properties of text? What are sufficient conditions for applying SVMs to text-classification problems successfully?
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Citations
Cited by
Text classification by augmenting bag of words (BOW) representation with co-occurrence feature
Department of Computer Science and Engineering, G. E. C., George K, S., & Joseph, S. (2014) · IOSR Journal of Computer Engineering · doi:10.9790/0661-16153438 · Cited by 94
Metadata
- Authors
- Joachims, T.
- Venue
- Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2001-09
- Pages
- 128-136
- OpenAlex
- W2034170092
- Cited by
- 224
- References
- 0
- FWCI
- 6.93
- Type
- conference paper
- Language
- en
- Open access
- closed
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How to cite
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- CoChat. (2026). Citation check: A statistical learning learning model of text classification for support vector machines. https://app.cochat.ai/papers/10.1145/383952.383974
- The paper
- Joachims, T. (2001). A statistical learning learning model of text classification for support vector machines. Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval. https://doi.org/10.1145/383952.383974
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
