| tech,22-3-P97-1006,ak | mixture models </term> , and employ the <term> | EM algorithm | </term> to efficiently estimate <term> parameters | #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. | |
| model,15-3-P97-1006,ak | statistical hypothesis testing </term> over <term> | finite mixture models | </term> , and employ the <term> EM algorithm | #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. | |
| other,27-3-P97-1006,ak | 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. | |
| tech,11-2-P97-1006,ak | finite mixture model </term> based on <term> | soft clustering | </term> of <term> words </term> . We treat the | #29089 We 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 | #29092 We define for each category a finite mixture model based on soft clustering ofwords. |
