ML Anthology

Relevance Feedback Using Support Vector Machines

Harris Drucker, Behzad Shahraray, David C. Gibbon
ICML 2001 pp. 122-129
/icml/2001/drucker2001icml-relevance/

Abstract

We show that support vectors machines (SVM’s) are much better than conventional algorithms in a relevancy feedback (RF) environment in information retrieval (IR) of text documents. We track performance as a function of feedback iteration and show that while the conventional algorithms do very well in the initial feedback iteration if the topic searched for has high visibility in the data base, they do very poorly if the relevant documents are a small percentage of the data base. SVM’s however do very well when the number of documents returned in the preliminary search is low and the number of relevant documents is small. The competitive algorithms examined are Rocchio, Ide regular, and Ide dec-hi. 1.

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Cite

Text

Drucker et al. "Relevance Feedback Using Support Vector Machines." International Conference on Machine Learning, 2001.

Markdown

[Drucker et al. "Relevance Feedback Using Support Vector Machines." International Conference on Machine Learning, 2001.](https://mlanthology.org/icml/2001/drucker2001icml-relevance/)

BibTeX

@inproceedings{drucker2001icml-relevance,
  title     = {{Relevance Feedback Using Support Vector Machines}},
  author    = {Drucker, Harris and Shahraray, Behzad and Gibbon, David C.},
  booktitle = {International Conference on Machine Learning},
  year      = {2001},
  pages     = {122-129},
  url       = {https://mlanthology.org/icml/2001/drucker2001icml-relevance/}
}