Sentence examples for train soft from inspiring English sources

We believe that the addition of social dynamics to a study program can train "soft" social skills like leadership and motivation, which are crucial to a student's future success.

(c) Trained soft decision tree structure.

Enterprising leaders need of social value and impact training, softer skills around communication and people developmen,t and often a softer marketing approach.

As we all know, training soft-margin classifier is a constrained optimization problem as formula (9).

Very few privately provided training programs surveyed as part of this study provide on-the-job training, soft-skills training, or intermediation services, even though international best practices show that these are critical to improve the employability and employment chances of unemployed individuals.

Lick This provides three exercises – flicking a light switch, boinging a beach ball, and rotating the handle of a pencil sharpener – to train your soft palate in the arts of licky love.

We train different soft computing classifiers, including decision trees (DT), k-nearest neighbors (k-NN), support vector machines (SVM) and an own developed fuzzy classifier and compare our method with conventional multi-class ML.

Along the same line of thought, we may consider applying sequential training to the student after training with soft targets from the teacher using cross-entropy has converged.

The GB conversion method proposed here uses a parallel training set but does not require time alignment between the source and target training vectors since it is trained using soft correspondence between them, rather than matched pairs.

However, when the student was trained with soft targets from the sequentially trained teacher, only around 59 and 70%% of the reduction in WER seen when comparing the sequentially trained teacher to the hard target-trained baseline DNN on Hub5'00-SWB and RT03S-FSH, respectively, was transferred to the student.

One student DNN was trained with soft targets and the other was trained with hard targets generated by assigning a posterior probability of 1.0 to the class having the strongest prediction by the teacher.