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In template selection, the memory overhead was around 6.1 MB when 20% templates were selected to be the representatives.
In template selection, the goal is to choose a small subset of templates as the representatives for the full set of training templates.
Uludag et al. [10] proposed two typical methods for automatic template selection: the first one, DEND, employs a hierarchical clustering strategy to choose a template set that could be best represents the intra-class variations.
The binding of p33 to RII-SL is required for template selection, the recruitment of the viral RNA for replication and for assembly of the active replicase complex.
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In this section, the template selection and the polyhedron establishment will be introduced in detail.
Although cis-preference is the prevailing rule for template selection by the L1 enzymatic machinery, other transcripts can occasionally be retrotransposed in trans [ 6, 9, 15– 20].
Images selected by using template selection methods constitute the template database and the remaining images of the finger constitute the query database.
Future study is to be done on more samples to further verify the performance of both the new template selection method and the proposed framework.
In Table 2, we show how the threshold value t of Equation 19 for removing the GMM outliers affected the recognition performance, where the template selection method was MLTS with the KL distance, and 20% template representatives were selected.
There are a few studies have been done to deal with the problem of template selection to solve the first challenge.
In Table 2, we use minutiae-based method, for DT, TT, FT, our proposed template selection method outperforms the MDIST, and the average EERs is lower than original matcher.
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