"labeling costs" is a correct and usable phrase in written English.
It refers to the expenses associated with labeling or identifying items or products. Example: The company decided to cut down on labeling costs by using generic labels instead of custom ones for their products.
We study their use to minimize labeling costs of data and to enable improvements on performance over time.
Moreover, we introduce a new metric to examine annotator quality, which can be used for finding good annotators to enhance consensus quality and reduce crowd labeling costs.
An automatic prior segmentation of the point clouds into rooms and outside area filters large-scale outliers and yields priors for the definition of labeling costs for the energy minimization.
The ideas of binary switching algorithm (BSA), which aims at labeling costs optimization, are presented in [6, 11].
Additionally, our method does not require the emotional speech of new speakers, and can save enormous data collection and labeling costs.
He argues that labeling costs would be low and that consumers should be fully informed about what is in their food.
A fully convolutional neural network is trained for semantic segmentation of pixels that provides labeling cost associated with each pixel.
The aim of this study was to estimate the performance of active learning algorithm for detecting blueberry damage using hyperspectral transmittance data with the very low labeling cost.
However, the BSA based labeling optimization evaluates the labeling cost assuming that full a priori information is available.
In [6], labeling cost refers to pairwise error probability in AWGN channels with full a priori information.
However, it requires a fully labeled training set, which is prohibitive when the labeling cost is high and, besides that, it requires labeled instances from all classes.
