Sentence examples for reducing classification from inspiring English sources

In the present literature, there exists no other distinct way to optimally select the features without reducing classification performance.

Empirical results confirm that LB can statistically significantly outperform alternative methods in terms of reducing classification error.

For future work, we plan to optimize the generation of action templates by eliminating redundant information (i.e., clustering similar instances or forgetting unused instances), and therefore reducing classification times.

However, fMRI has much better spatial resolution, and it's possible that the spatial information could also contribute to reducing classification delay [30].

Our aim here was to provide an example of the kinds of features that may be useful for reducing classification delays.

Specifically, we explored the usefulness of including different features, such as temporal history, temporal gradient, NIRS signals (oxy-Hb, deoxy-Hb, total-Hb and CBSI corrected signal), and spatial information, for reducing classification delay and thereby improving accuracy.

The usage of the reduced set of features and SVM classifier gave only slightly reduced classification performances, which did not differ from the full sets of features.

We observe that the proposed posterior smoothing in the acoustic space can further reduce classification errors.

Feature dimension reduction using principal component analysis (PCA) may provide effective classification performance and reduced classification time.

On the other hand, having too many regions reduces classification efficiency and may also lower recognition accuracy due to too much unnecessary location information being included.

As a result, LB, which is built on these classifiers, can significantly reduce classification error, compared with the traditional bagging (TB) approach.