The phrase "abstract features" is correct and usable in written English.
It can be used in contexts such as discussing theoretical concepts, design elements, or characteristics that are not concrete or tangible.
Example: "In our analysis, we focused on the abstract features of the artwork, such as its use of color and form."
Alternatives: "conceptual attributes" or "theoretical characteristics".
The word grammar also denotes the study of these abstract features or a book presenting these rules.
Aside from offering an array of landscape gardening solutions and attributes he also proposes more abstract features such as synthetic spirituality and symbolic gestures of generosity.
A. Previous attempts put very abstract features of human intelligence into a machine: chess playing, mathematical theorem-proving and natural language processing.
Mr. Hannas's logic goes like this: because East Asian writing systems lack the abstract features of alphabets, they hamper the kind of analytical and abstract thought necessary for scientific creativity.
These abstract features must, Rawls says, have concrete institutional realizations.
Such algorithms develop a layered, hierarchical architecture of learning and representing data, where higher-level (more abstract) features are defined in terms of lower-level (less abstract) features.
He placed a one-inch ad in the back of The Sporting News, asking three dollars for the "Baseball Abstract: Featuring 18 Categories of Statistical Information That You Just Can't Find Anywhere Else".
The idiom of the later groups is strongly abstract, featuring a layered construction, controlled gestures, and the use of superimposed mediums, but all allude to the interplay of natural forces or elements.
Just as those blue dots in the laundry detergent are only an abstract feature until you know that they provide the benefit of cleaner clothes, Cantor wanted to show that Republican fiscal policies "make life work" for average Americans, a group of whom he had assembled for the lunch.
Recent results show that this type of abstract feature transformations is not uncommon [12].
DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstract feature representation and a multi-class classification.
