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The semantics of a feature model is the set of feature configurations that the feature model permits. The most common approach is to use mathematical logic to capture the semantics of a feature diagram. [5] Each feature corresponds to a boolean variable and the semantics is captured as a propositional formula. The satisfying valuations of this ...
Feature-driven development is built on a core set of software engineering best practices aimed at a client-valued feature perspective. Domain object modelling. Domain object modeling consists of exploring and explaining the domain of the problem to be solved. The resulting domain object model provides an overall framework in which to add features.
Discretization is also concerned with the transformation of continuous differential equations into discrete difference equations, suitable for numerical computing. The following continuous-time state space model
In pattern recognition and machine learning, a feature vector is an n-dimensional vector of numerical features that represent some object. Many algorithms in machine learning require a numerical representation of objects, since such representations facilitate processing and statistical analysis.
Feature standardization makes the values of each feature in the data have zero-mean (when subtracting the mean in the numerator) and unit-variance. This method is widely used for normalization in many machine learning algorithms (e.g., support vector machines , logistic regression , and artificial neural networks ).
The feature store is where the features are stored and organized for the explicit purpose of being used to either train models (by data scientists) or make predictions (by applications that have a trained model). It is a central location where you can either create or update groups of features created from multiple different data sources, or ...
Exclusive feature bundling (EFB) is a near-lossless method to reduce the number of effective features. In a sparse feature space many features are nearly exclusive, implying they rarely take nonzero values simultaneously. One-hot encoded features are a perfect example of exclusive features.