Sentence examples for performances of learning from inspiring English sources

Experimental results show good performances of learning algorithms and set them as a solid alternative to the computationally intensive and complex to implement geometrical techniques.

This approach recognizes possible variations in the relative performances of learning algorithms across studies or fields of application.

Experimental findings endorse that the employment of feature selection enhances the performance of learning model.

So by removing less important and redundant data, the performance of learning algorithm could be improved.

The first attempt to characterize datasets for evaluating the performance of learning algorithms was made by Rendell et al. [24].

Kernel trick improves largely the performance of learning system including recognition, clustering, prediction through the nonlinear kernel mapping from the input data space to output data space.

We show here how to obtain deeper insight into the comparative performance of learning methods through a complementary parametric approach to data analysis.

At the same time, our methods allow direct comparisons between the performance of learning algorithms in different experiments, since no randomness is present.

In such cases transfer learning would greatly improve the performance of learning by avoiding much expensive data-labeling efforts, however, many open questions remain for future research [88].

The goal of Co-Training is to enhance performance of learning algorithm when only a small set of labeled examples is available.

The performance of learning between classic SRM and simplified SRM models can be measured with the mean square error (MSE) for normalized weights after training.