We define for each category a
<term>
finite mixture model
</term>
based on
<term>
soft clustering
</term>
of
<term>
words
</term>
.
#29084We define for each category a finite mixture model based on soft clustering of words.
tech,11-3-P97-1006,ak
We treat the problem of classifying documents as that of conducting
<term>
statistical hypothesis testing
</term>
over
<term>
finite mixture models
</term>
, and employ the
<term>
EM algorithm
</term>
to efficiently estimate
<term>
parameters
</term>
in a
<term>
finite mixture model
</term>
.
#29105We treat the problem of classifying documents as that of conducting statistical hypothesis testing over finite mixture models, and employ the EM algorithm to efficiently estimate parameters in a finite mixture model.
model,30-3-P97-1006,ak
We treat the problem of classifying documents as that of conducting
<term>
statistical hypothesis testing
</term>
over
<term>
finite mixture models
</term>
, and employ the
<term>
EM algorithm
</term>
to efficiently estimate
<term>
parameters
</term>
in a
<term>
finite mixture model
</term>
.
#29124We treat the problem of classifying documents as that of conducting statistical hypothesis testing over finite mixture models, and employ the EM algorithm to efficiently estimate parameters in a finite mixture model .