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 afinite mixture model based on soft clustering of words.
tech,11-2-P97-1006,ak
We define for each category a
<term>
finite mixture model
</term>
based on
<term>
soft clustering
</term>
of
<term>
words
</term>
.
#29089We define for each category a finite mixture model based onsoft clustering of words.
other,14-2-P97-1006,ak
We define for each category a
<term>
finite mixture model
</term>
based on
<term>
soft clustering
</term>
of
<term>
words
</term>
.
#29092We define for each category a finite mixture model based on soft clustering ofwords.
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 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
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>
.
#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
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>
.
#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
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>
.
#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.
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 afinite mixture model.