Sentence examples for training in the case of from inspiring English sources

While some key cyclists have been absent at world championships, often because of road races or road training in the case of Mark Cavendish and Bradley Wiggins or following injury such as Chris Hoy's in 2009, it is possible to take Jason Kenny's performances as a micro sample.

In addition, Table 5 also compares both DNN-based acoustic modeling approaches when speaker adaptation (and conventional discriminative training in the case of BN-HMM/GMM) is exploited during training and decoding (performance denoted as final).

The results suggest that the value of extended mannequin simulation training in the case of LMA placement is limited.

The results of this study suggest that the value of extended low-fidelity mannequin training in the case of LMA placement is limited.

Similarly, among private providers, receiving drug-detailing visits (and CME training, in the case of formal-sector providers) for diarrhea treatment within the last 6 months was predictive of recommending zinc treatment (P = .01) but not for having correct knowledge of the duration or dosage of zinc.

Besides training interventions (e.g. lip-reading training in the case of hearing problems) and modifications of the environment (e.g. increasing luminance in the case of vision problems), assistive devices (e.g. walkers, glasses and hearing aids) offer considerable potential (Schieber et al. 1991).

The email included links to all websites to purchase tickets, along with approximate costs, and information about where to change trains in the case of DART.

However, the fundamental insight of this article is that in protein homology detection this need not be the end of the story, that is, we can actually harness the power of a simple evolutionary model to generate new, additional artificial training data in the case of training HMMs to recognize remote protein homologs.

To realize a practicable training algorithm for the new HMM, this paper first introduces an entropic definition of the a priori probability of the model and accordingly a maximum a posteriori (MAP) training strategy, and then designs an MAP training algorithm in the case of fixed state number based on the deterministic annealing (DA) technique.

Both lightly trained and untrained groups provided a venous blood sample at 08 00 hours under resting conditions (i.e., at least 24 hours after a training session in the case of lightly trained athletes).

The present paper addresses the last, rarely considered issue, namely comparison of methods to prevent multi-layer perceptron neural networks from overfitting of the training data in the case of daily catchment runoff modelling.