Sentence examples for optimise the classification from inspiring English sources

The hybrid method is able to select the most robust features, to adjust automatically the NN architecture and to optimise the classification performance.

Even though Random Forest does not need an internal test set for training, as do other methods such as an artificial neural network, we use an internal test set to adjust a threshold to optimise the classification in terms of the Matthews Correlation Coefficient, see below.

The identification of PTMs for each sequence segment of the query protein is done using the set of feed-forward artificial neural networks (ANN), which are trained with Back-Propagation (BP) learning algorithm (Rumelhart et al. 1986) to optimise the classification accuracy between the positive and the negative samples in the randomly chosen training subset of sequence segments.

It incorporates artificial intelligence for optimising the classification of observed particles.

The MCS could optimise the correct classification rate, reaching an accuracy of 91.7%.

However, our results proved that, although a single classifier trained separately with dynamic and morphological features achieves a low overall accuracy, the MCS could optimise the correct classification rate: dynamic features with the decision tree gave 60% accuracy, morphological features with the Bayes classifier gave 80% accuracy, and the MCS gave 91.7% accuracy.

The microarray design strategy was optimised for the classification of clinically relevant enterobacteria.

An MCS can optimise the accuracy for breast lesion classification combining morphological features and dynamic information.

Instead of fitting all the data, classification algorithms optimise the approximating function to separate the best and worst individuals.

This work proposes a methodology to learn a speech representation from data by optimising a filter bank, in order to improve results in the classification of stressed speech.

The principal aims were (1) to assess the classification accuracy of rCBF indices acquired prior to and following surgical intervention and (2) to optimise the amount of data required to maintain accurate classification.