Sentence examples for more robust classifiers from inspiring English sources

Disparities in performance of classifiers on different datasets may indicate that review spam detection may benefit from additional cross domain experiments to help develop more robust classifiers.

The benefit of allowing more lenient cutoffs is twofold: in a classification context it permits the discovery of more robust classifiers, and in a gene discovery context it enables the detection of genes with inconsistent aberrant expression.

This technique can be used to build more robust classifiers in cases (such as ours) where large imaging datasets can easily be collected but where it is much harder to manually annotate these images.

The former provides the ability to learn multiple noise patterns, and the latter encourages a more robust classifier.

Finally, we introduce RevealDroid*, a more robust classifier that implements several techniques proposed in other adversarial learning domains.

A bigger sample size allows for the training of a more robust classifier and might improve the prediction accuracy (PA) on the test set.

Here we design a new nonparallel support vector machine (U-NSVM) that can exploit prior knowledge embedded in the universum to construct a more robust classifier for training.

In addition, we also propose a local neighbourhood search (LNS) algorithm to obtain a more robust classifier if the data is known to have a non-normal distribution.

None of the tumours were excluded for further analysis as variation in tumour tissue composition would allow the construction of a more robust classifier.

This is a desirable property for building a more robust classifier downstream of our analysis pipeline (Step 2 and Step 5).

Harris et al. [ 14] presented the Genetic Learning Across Datasets concept (GLAD), a new Semi-Supervised Learning (SSL) method for combining independent annotated datasets and unannotated datasets with the aim of identifying more robust sample classifiers.