@device(postscript)
@libraryfile(Mathematics10)
@libraryfile(Accents)
@style(fontfamily=timesroman,fontscale=11)
@pagefooting(immediate, left "@c<Technical Report 1996>",
center "@c<Computer Science>",
right "@c<Carnegie Mellon>")
@heading(A Probabilistic Analysis of the Rocchio Algorithm
with TFIDF for Text Categorization)
@heading(CMU-CS-96-118)
@center(@b(Thorsten Joachims))
@center(March 1996)
@center(FTP: CMU-CS-96-118.ps)
@blankspace(1)
@begin(text)

A probabilistic analysis of the Rocchio relevance feedback algorithm, one of
the most popular learning methods from information retrieval, is presented
in a text categorization framework. The analysis results in a probabilistic
version of the Rocchio classifier and offers an explanation for the TFIDF
word weighting heuristic. The Rocchio classifier, its probabilistic variant
and a standard naive Bayes classifier are compared on three text
categorization tasks. The results suggest that the probabilistic algorithms
are preferable to the heuristic Rocchio classifier.

@blankspace(2line)
@begin(transparent,size=10)
@b(Keywords:@ )@c<Text categorization, relevance, feedback, naive Bayes
classifier, information retrieval, vector space retrieval model, machine
learning>
@end(transparent)
@blankspace(1line)
@end(text)
@flushright(@b[(26 pages)])