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"feature representation" is a correct and usable phrase in written English.
You can use it to refer to an organized representation of facts, features, or characteristics about a certain object or phenomenon. For example: "Machine learning algorithms require high-dimensional feature representation for accurate prediction."
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Furthermore, we introduce an effective feature representation for infrared objects.
We then chose the first ten frequencies for feature representation.
These terms allow for a more discriminatory shared feature representation.
LDABoost.1 uses VSM model and words for feature representation.
The remarkable difference is the feature representation for a face.
An acquired skill includes both the learned actions and the learned slow feature representation.
The BoW-based image feature representation is highly sensitive to various distortion types and levels.
This FCN-based architecture addresses two main tasks, feature representation and cascaded outlier detection.
Due to the designed hierarchical strategy, the discriminative power of feature representation can be promoted.
And we consider the histogram-based feature as our final feature representation for each face image.
This paper proposes a novel feature representation model for accurate pose estimation.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com