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As the PC treatment alternatives described in our choice sets were very realistic, each treatment modality had several fixed attribute levels (i.e., one fixed attribute level for 'risk of other side effects', one fixed attribute level for 'frequency of PSA testing', and one fixed attribute level for 'treatment aim').
Two new features fix that.
Likewise, we test edge features by fixing trigger features.
Fixing multiple attributes based on benchmarking approaches by using other attributes limits reliable real-time skin detection.
"Fixed features" are truly fixed at the scene.
Bugfix releases, which introduce no new features but fix bugs.
The feature selection method enlarged the fixed feature set.
Feature-based machine learning algorithms require the instances to have a fixed set of attribute values from a feature space.
"It is their improvised exchange with their subjects, not a kit of fixed and essential attributes, that distinguishes their work".
Features and attributes can vary in both space and time.
In addition to these features, different attributes were used.
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CEO of Professional Science Editing for Scientists @ prosciediting.com