The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Text categorization with Support Vector Machines: Learning with many relevant features
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Citations
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Research works printed but not linked: 7
- T. Joachims. A probabilistic analysis of the rocchio algorithm with tfidf for text categorization. In International Conference on Machine Learning (ICML), 1997.
- J. Kivinen, M. Warmuth, and P. Auer. The perceptron algorithm vs. winnow: Linear vs. logarithmic mistake bounds when few input variables are relevant. In Conference on Computational Learning Theory, 1995. · DOI
- T. Mitchell. Machine Learning. McGraw-Hill, 1997.
- J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.
- J. Rocchio. Relevance feedback in information retrieval. In G. Salton, editor, The SMART Retrieval System: Experiments in Automatic Document Processing, pages 313–323. Prentice-Hall Inc., 1971.
- G. Salton and C. Buckley. Term weighting approaches in automatic text retrieval. Information Processing and Management, 24(5):513–523, 1988. · DOI · Paper page
- Y. Yang and J. Pedersen. A comparative study on feature selection in text categorization. In International Conference on Machine Learning (ICML), 1997.
Cited by
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
Thumbs up?: sentiment classification using machine learning techniques
Pang, B., Lee, L., & Vaithyanathan, S. (2002) · Proceedings of the ACL-02 conference on Empirical methods in natural language processing - EMNLP '02 · doi:10.3115/1118693.1118704 · Cited by 7,024
Bag of Tricks for Efficient Text Classification
Joulin, A., Grave, E., Bojanowski, P., & Mikolov, T. (2017) · Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers · doi:10.18653/v1/e17-2068 · Cited by 4,545
A survey of the recent architectures of deep convolutional neural networks
Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09825-6 · Cited by 3,137
Text classification from labeled and unlabeled documents using EM
Nigam, K., Mccallum, A. K., Thrun, S., & Mitchell, T. (2000) · Machine Learning · doi:10.1023/a:1007692713085 · Cited by 2,736
Metadata
- Authors
- Joachims, T.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 1998
- Pages
- 137-142
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2149684865
- Cited by
- 7,999
- References
- 2
- FWCI
- 55.60
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Joachims, T. (1998). Text categorization with Support Vector Machines: Learning with many relevant features. Lecture Notes in Computer Science. https://doi.org/10.1007/bfb0026683
Data sources
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.
