Sentence examples for properties of training from inspiring English sources

The statistical properties of training, validation and test data play an important role in assuring optimal performance in artificial neural networks (ANNs).

This is because the goal of traditional classification modeling is to construct a function (or a classifier) based on the properties of training data so as to make as few errors as possible when being used to predict the class membership of new samples [ 2].

Their models make assumptions about the physical properties of train carriages and their passengers, and both assume that the radiation is uniformly distributed rather than clumped into "hot spots".

GPC models are Bayesian extensions of logistic regression that aim to learn statistical properties of a set of "training" data that enables accurate prediction of the label of unseen ("test") data using the rules of probability.

The theory yields several iterative, convergent, learning algorithms, a clear understanding of the generalization properties of the trained autoencoders, and can equally be applied to the hetero-associative case when external targets are provided.

This approach allows to preserve the stochastic properties of the training series in multiple-step ahead prediction (Ben Taieb et al. 2010).

Based on the properties of the training data, the algorithms will be able to classify these new molecules as active or inactive.

Thus properties of the training set may significantly affect the performance of the classifier.

Topological and biological properties of these training graphs are then summarized as features.

Those who agreed were sent a link to an online questionnaire concerning desirable properties of the training website.

Further analysis shows that the sequence properties of the training and test datasets are quite similar (Supplementary Figs. S1– S1).