A Comparison of Classifiers and Document Representations for the Routing Problem

@inproceedings{Schtze1995ACO,
  title={A Comparison of Classifiers and Document Representations for the Routing Problem},
  author={Hinrich Sch{\"u}tze and David A. Hull and Jan O. Pedersen},
  booktitle={SIGIR},
  year={1995}
}
In this paper, we compare learning techniques based on statistical classification to traditional methods of relevance feedback for the document routing problem. We consider three classification techniques which have decision rules that are derived via explicit error minimization: linear discriminant analysis, logistic regression, and neural networks. We demonstrate that the classifiers perform 1015% better than relevance feedback via Rocchio expansion for the TREC-2 and TREC-3 routing tasks… CONTINUE READING
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