Sentence examples for parameters trained on from inspiring English sources

Typical time necessary to obtain the filter bank and the associated parameter statistics is less than 10 min. It should also be noted that templates and parameters trained on a data set from one experiment can often be used for detection of EPSCs in other similar experiments.

For those transcripts without any BLASTx hit against known databases, the best potential coding region was predicted using the software BestORF with parameters trained on Arabidopsis ESTs.

First, our algorithm uses parameters trained on public data set as the starting point, which is expected to be close to the real one, and therefore, the necessary iterations are smaller than the traditional Newton method (starting from all zeros).

To evaluate the performance of TruHMM and the robustness of its parameters on different organisms and datasets, we first applied TruHMM with the parameters trained on the E. coli K12 dataset to the earlier directional RNA-seq datasets of H. pylori generated under five different culture conditions [ 24].

Equally importantly, the performance of TruHMM also is very robust as we have demonstrated that the E. coli-trained parameters can be used to assemble the transcripts in H. pylori and vice versa, while achieving in both the cases exactly the same results as being done using the parameters trained on their own verified operons.

It can be concluded form the above discussion that even though most of the fixed parameters were trained on few frames (one in most cases), they were still able to perform well on all the examined sequences.

Despite its simplicity, our model has good generalization power when its parameters are trained on a diverse data set.

The ab initio GlimmerHMM, AUGUSTUS, and SNAP parameters were trained on all A. oryzae RIB40 gene models, while GeneMark + ES performed an iterative self-training procedure.

In order to gauge the benefits of the proposed PME parameters, a classifier trained on EEG 'spectral peak' parameters was used as benchmark.

Observation parameters can be trained on these resources and then transferred to initialize the supervised training process on the target labeled data.

In Fig. 3(b), we also plot histograms of learned parameters from networks trained on data with these three sample sizes.