Negative filter
finite, mixture, model 6
(192.1 per million)
tech,11-2-P97-1006,ak
finite mixture model
</term>
based on
<term>
soft clustering
</term>
of
<term>
words
</term>
. We treat the
#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.
tech,11-3-P97-1006,ak
classifying documents as that of conducting
<term>
statistical hypothesis testing
</term>
over
<term>
finite mixture models
</term>
#29105We treat the problem of classifying documents as that of conductingstatistical hypothesis testing over finite mixture models, and employ the EM algorithm to efficiently estimate parameters in a finite mixture model.
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.