Sentence examples for normal instances from inspiring English sources

Such techniques learn a discriminative boundary around the normal instances using a one-class classification algorithm.

Outlier detection techniques typically assume that outliers in data are rare compared to normal instances.

The earliest algorithms used for outlier detection were statistical approaches which assume that normal instances occur in high probability regions, while anomalies occur in low probability regions.

Then, instead of an isolated instance of self-interest, it would be a normal instance of compassion.

For instance, if the map were oriented on top of a nursing home, it would be expected to show a higher than normal instance of cancer, but it would not represent some kind of "cancer cluster".

Running SQL Server images incurs some extra cost on top of Google's normal instance costs, though.

If none of the classifiers are confident in classifying the test instance as normal, the instance is declared to be anomalous.

Usually, the number of normal data instances is more than the number of anomaly data instances.

Patients may also be 'reassured' by lack of follow-up by doctors/the health-care system, interpreting lack of communication to mean that 'all is normal' in instances when this is not the case.

This makes sense as the XSEDE cloud is primarily aimed at scientific computing and data visualization which requires allocations that are quite different than the normal EC2 instances.

Such an approach is based on the following hypothesis according to Chandola, Banerjee and Kumar [23]: normal data instances lie close to their closest cluster centroid, while outliers are far away from their closest cluster centroid.