#29089We define for each category a finite mixture model based onsoft clustering of words.

other,14-2-P97-1006,ak

based on
<term>
soft clustering
</term>
of
<term>

words

</term>
. We treat the problem of classifying

#29092We define for each category a finite mixture model based on soft clustering ofwords.

model,15-3-P97-1006,ak

statistical hypothesis testing
</term>
over
<term>

finite mixture models

</term>
, and employ the
<term>
EM algorithm

#29109We treat the problem of classifying documents as that of conducting statistical hypothesis testing overfinite mixture models, and employ the EM algorithm to efficiently estimate parameters in a finite mixture model.

tech,22-3-P97-1006,ak

mixture models
</term>
, and employ the
<term>

EM algorithm

</term>
to efficiently estimate
<term>
parameters

#29116We treat the problem of classifying documents as that of conducting statistical hypothesis testing over finite mixture models, and employ theEM algorithm to efficiently estimate parameters in a finite mixture model.

other,27-3-P97-1006,ak

algorithm
</term>
to efficiently estimate
<term>

parameters

</term>
in a
<term>
finite mixture model
</term>

#29121We treat the problem of classifying documents as that of conducting statistical hypothesis testing over finite mixture models, and employ the EM algorithm to efficiently estimateparameters in a finite mixture model.