Sentence examples for improve the generalisation from inspiring English sources

There are different approaches that can be used to improve the generalisation performance of single classifiers.

One possibility to improve the generalisation of the terminology would be to include all diagnostic classes from publicly available terminology resources.

With prior probabilities based on the belief network the apparent error rate was 15% corresponding to a correct classification of 39 out of 46 tumours, the cross validation and 632+ error rates were 32%and37%7% respectively, indicating that the prior probabilities measurably improve the generalisation performance of the classifier.

In this work we propose an algorithm to evolve a robust speech representation, using a dynamic data selection method for reducing the computational cost of the fitness computation while improving the generalisation capabilities.

This fact improve the network generalisation capabilities and speed up the convergence of the learning process.

For example, in order to improve the way patients are informed of cancer diagnosis, the Cancer Plan 2009 2013 approved the generalisation of the 'Diagnostic Disclosure Procedure', 40 67 whose context and content are precisely defined.

These features may improve the internal validity of these studies, however, for a number of reasons, we would caution against generalisation of the effects observed in these studies to other contexts.

A multiple-case study was chosen to capture the various types of hospitals involved in DOTS strategy and to improve generalisation of the result.

Interestingly, this word error rate reduction scheme did not improve generalisation performance for boosting, while the simplest approach of all, bagging, performed the best.

We tested two manipulations designed to improve generalisation of relearned words by introducing greater variation into the learning experience.

The methodology of reducing the size of the training set by excluding samples, both in semi-supervised or unsupervised settings, is called "data pruning" and has been previously used to avoid overfitting and improve generalisation [20], [21].