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To further evaluate the method, evaluation sets containing LTRs from opossum and other groups were used (see below).
Several representative applications are included to evaluate the method.
In this section, we evaluate the method proposed in this paper using real datasets.
In the case of existing facial-level annotations, we quantitatively evaluate the method.
To evaluate the method without over-fitting, we use threefold cross-validation.
In order to evaluate the method, it has been applied in a case study.
We present experiments with artificial data and real-world gene expression data to evaluate the method.
A case study was carried out for the OC4 jacket substructure to evaluate the method.
Experimental data from several different sources were used to evaluate the method.
TLBO, besides of its easier application, outperformed earlier optimization algorithms, which were used to evaluate the method effectiveness.
Experiments using speaker identification (SID) and automatic speech recognition (ASR) systems were conducted to evaluate the method.
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