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Cited by 7,999

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

Joachims, Thorsten·1998·Lecture Notes in Computer Science

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Crossref · 10.1007/bfb0026683

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Citations

Cites

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Research works printed but not linked: 7

  1. T. Joachims. A probabilistic analysis of the rocchio algorithm with tfidf for text categorization. In International Conference on Machine Learning (ICML), 1997.
  2. 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
  3. T. Mitchell. Machine Learning. McGraw-Hill, 1997.
  4. J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.
  5. 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.
  6. G. Salton and C. Buckley. Term weighting approaches in automatic text retrieval. Information Processing and Management, 24(5):513–523, 1988. · DOI · Paper page
  7. Y. Yang and J. Pedersen. A comparative study on feature selection in text categorization. In International Conference on Machine Learning (ICML), 1997.

Cited by

Items 101–105 of 105

Metadata

Authors
Joachims, T.
Venue
Lecture Notes in Computer Science
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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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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