Sentence examples for function of features from inspiring English sources
The relationship between health, disability, and employment is not just a function of individual characteristics but also a function of features of the workplace and physical environment.
In many cases, our attitude regarding risks is a function of features of the situation that are not correlated directly with the risk level, such as our perceived level of control and our familiarity with the activity (Tversky, Kahneman 1974; Tversky, Kahneman 1981; Slovic 1987; Weinstein 1989).
Similar behavior was obtained using different classifiers, indicating that our results are a function of features and not of classifiers.
These methods enable researchers to identify differences in outcomes as a function of features of the intervention and/or patient population, and, in so doing, support efforts to create effective and efficient disease management strategies.
2) The transition time is determined by a single transition rate λ, and the outcome (blue vs red cell) is determined by a probability p, which might again be a function of features such as cell density.
We model the problem evaluating the possibility a subgraph is a true complex as a linear regression function, f (x ) = ω T ⋅ x, where f(x) is the linear function of features and ω T is the weight vector of the dimension corresponding to the number of the features.
However, car body displacement is a function of characteristic features of the rail vehicle such as suspension properties, masses, and inertia.
VS focuses on the module of pattern classification, while i-ICP pays more attention on the function of feature extraction.
The SVM method has no function of feature elimination.
Since top features or predictors from two-sample proportion test are based on only binary information, the density function of feature scores f jY (x) is excluded from the computation of P(X j | Ψ jY ) for these predictors.
Third, the evaluation is transparent since the classification accuracy was examined as a function of feature set size (from top one to ten pathways, ranked by t-test on pathway-level aggregated data), instead of at a fixed arbitrary number of features, or an optimized number of features that results in the best performance in the validation dataset [ 8].
