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We propose three methods of template selection and compression to address this problem.
In this section, the template selection and the polyhedron establishment will be introduced in detail.
The performances of template selection and compression exhibited a dependency on the local distance measures.
The flowcharts of the above-discussed three template selection and compression methods are given in Figure 5.
Figure 8 Phone accuracies (percent) for methods of template selection and compression with KL and LLR local distances.
Lumini and Nanni [11] presented a novel clustering method for template selection, and this method is better than MDIST in their study.
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Uludag et al. [10] proposed two methods for template selection: DENT and MDIST, but MDIST method gets a better performance than DEND in their study.
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 the first part of this work different measures of similarity in template selection were explored and methods for homology modelling, docking and refinement of aminergic GPCR-ligand complexes were developed and evaluated by comparing models of the D3-R/eticlopride complex with the crystal structure.
On the TIMIT task, we provide a detailed account of the factors in the proposed template matching methods that affect the rescoring performance, including local distances, number of GMMs employed for frame labeling, template selection, compression methods and their interactions with the local distances, and the percentage of selected template representatives.
The main contributions of this article to the fingerprint verification are (1) a template selection method is proposed, and this method is more robust and effective than the MDIST.
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