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A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization (1997)
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BibTeX
@INPROCEEDINGS{Joachims97aprobabilistic,
author = {Thorsten Joachims},
title = {A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization},
booktitle = {},
year = {1997},
pages = {143--151}
}
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Abstract
The Rocchio relevance feedback algorithm is one of the most popular and widely applied learning methods from information retrieval. Here, a probabilistic analysis of this algorithm is presented in a text categorization framework. The analysis gives theoretical insight into the heuristics used in the Rocchio algorithm, particularly the word weighting scheme and the similarity metric. It also suggests improvements which lead to a probabilistic variant of the Rocchio classifier. The Rocchio classifier, its probabilistic variant, and a naive Bayes classifier are compared on six text categorization tasks. The results show that the probabilistic algorithms are preferable to the heuristic Rocchio classifier not only because they are more well-founded, but also because they achieve better performance.
Keyphrases
rocchio algorithm probabilistic analysis text categorization rocchio classifier probabilistic variant rocchio relevance feedback algorithm text categorization task information retrieval naive bayes classifier text categorization framework heuristic rocchio classifier theoretical insight probabilistic algorithm
