Concordance

Query P97-1006 8 (256.1 per million)

model,6-2-P97-1006,ak	We define for each category a <term> finite mixture model </term> based on <term> soft clustering </term> of <term> words </term> .	#29084 We 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> .	#29089 We 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> .	#29092 We 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> .	#29105 We 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> .	#29109 We 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> .	#29116 We 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> .	#29121 We 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> .	#29124 We 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.


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