Sentence examples for machine learning purposes from inspiring English sources

WiGo holds on to certain information around events for machine learning purposes, so that the company can track a place that goes by multiple names or nicknames.

The PART class is however more relevant when the corpus is going to be used for machine learning purposes since parts of chemical names are not expected to be found in dictionaries.

In the following, we describe the methodology used for collecting crisis-related tweets, selecting a relevant subset of those, and letting human annotators tag them in order to be used for machine learning purposes.

Evernote says users can opt out of having their notes reviewed for machine learning purposes, but says that no user can opt out of having their notes read altogether.

But if Levine is right, its role is about to change fairly dramatically, where it will be processing data for machine learning purposes, acting as an adjunct to more immediate data processing needs.

GE makes huge machines like locomotives and gas turbines, generating tons of information, and they realized a few years ago, as the sensors on these massive machines generated ever-more data, it was going to require processing on the device itself at the edge, while moving only the most valuable data to the cloud for machine learning purposes.

LightSIDE is the core of EvoGrader, and is an open-source tool that can be modified; it provides different classification algorithms and validation metrics suited to many different purposes and may be used for any type of machine-learning purpose.

Time-series data on water consumption pose several challenges on applying machine learning algorithms for forecasting purposes.

To this purpose machine learning methods have been widely studied to design disruption prediction systems at ASDEX Upgrade.

It is clear that more curation data, despite the fact that they are not annotated for training machine-learning purposes, helps improve the coverage and system performance.

In total, it took approximately 685 hours for the two raters to generate the corpus that EvoGrader utilizes (note that these scored explanations were a byproduct of other research studies, and were not only generated for machine-learning purposes).