Sentence examples for improve training efficiency from inspiring English sources

In an effort to improve training efficiency, the military has focused much attention on the development of replicable and generalizable training systems.

Our experiments, which are performed on selected regions of images to improve training efficiency, show that GP can synthesize effective composite operators consisting of pre-designed primitive operators and primitive features to effectively detect objects in images and the learned composite operators can be applied to the whole training image and other similar testing images.

Such research demonstrates that gaze behaviour changes as a function of expertise and hints at the possibility that we may be able to identify what characterises expertise and how knowledge of this can be used to improve training practices, and efficiency, for future clinicians.

First, it can minimize the amount of training data and improve the training efficiency.

As a result, each sub-BPNN is only trained by a part of original data set so as to improve the training efficiency.

For this reason, some researchers has integrated FES into robotic-based rehabilitation to improve the training efficiency [3],[18],[19], in which an extra actuator besides the motor of the robot is available (considering the muscle under FES as a 'soft motor').

Based on such findings, it is being used by competitive and recreational athletes as an ergogenic aid to improve their training efficiency.

It improved the training efficiency, but the need for large-scale data gathering and extensive preparation creates a critical bottleneck.

This paper brings together effective normalization measures, visual features, and image attributes to construct a robust classification framework that minimizes the amount of training data needed while also improving the training efficiency.

In addition, instead of using the classical deep learning techniques, e.g., Convolutional Neural Network (CNN) [17], we proposed the use of R-CNN to account for the spatial context of facial regions; secondly, we improved the training efficiency of deep architecture by incorporating the MAC techinique.

A new technique is proposed to scale the error signal during the constructive learning process to improve the input-side training efficiency and to obtain better generalization performance.