Sentence examples for nonlinear datasets from inspiring English sources

Our aim is to address the issue of nonlinearity and to improve the classification accuracy of recently proposed small nonlinear datasets.

The results showed that NNs perform better than PLS for all the nonlinear datasets.

In addition, two nonlinear datasets were generated: NLG, where clusters of mixtures of Gaussians were generated for each class, and NLC, where the clusters follow a tighter checkers-patterned distribution.

Principal component analysis (PCA) has often been employed (Martella, 2006) in combination with a clustering algorithm that groups homogeneous classes on the basis of principal components, but this approach is insufficiently powerful to deal with nonlinear datasets.

If no label hypothesis is provided (as was the case for tissue embryological attribution, the uncovering of which was unsupervised), it is hard to imagine an unsupervised strategy that indicates the right tuning for unfolding the classes hidden in a nonlinear dataset.

Motivation: Nonlinear small datasets, which are characterized by low numbers of samples and very high numbers of measures, occur frequently in computational biology, and pose problems in their investigation.

Experiments conducted on both low-dimensional nonlinear toy dataset and high-dimensional textured images show that our approaches lead to very encouraging results.

Although the MC principle provided a valuable framework for the estimation of curvilinear distances in small, nonlinear, multi-dimensional datasets, its extension to large datasets needs careful consideration and adaptation.

ANNs have been selected thanks to their ability to discover and adequately interpret nonlinear relationships among datasets.

The ML XGBoost is a powerful statistical method of classification which detects nonlinear patterns in datasets with missing values.

The method using DP matching is expected to work well for speech datasets because nonlinear matching is necessary for speech data.