Sentence examples for features in the model from inspiring English sources
Inferring "biological relevance" from "model relevance" when modeling technique has an influence on the features in the model is then questionable.
The functional interpretation of any structural features in the model and its utilization for future studies can be made in the context of its measure of uncertainty.
We identify features in the model that are responsible for hormonal regulation, and use the model to identify and evaluate competing hypotheses associated with Wolff Chaikoff escape.
In an effort to develop operational tools that can address spacecraft charging issues, we also identify temporal features in the model output that can be directly linked to input parameter variations and model boundary conditions.
Implementation of new features in the model and new approach for its generation has eased updates and modifications of the model, reducing the time necessary to introduce them into the model and allowing it to keep pace with discussions on planned design changes.
Table 5 gives the word level features in the model, where Wi denotes the word in the i-th position in a sentence, Wab denotes the string of words from position a to b in a sentence, Ci denotes the category in the i-th position in a sentence, and Cab denotes the category label string of the words from position a to b in a sentence.
To effectively treat heterogeneity properties and multiscale features in the models, we integrate multiscale finite element methods with multi-element least-square HDMR for stochastic multiscale model reduction.
In order to convert between different formalisms it is important to have a standard representation format that preserves most of the features in the models.
As shown, including selected microarray features in the models clearly improved prediction performance over the purely clinical model indicating the valuable information containing in the microarray features.
To address this, we implement model averaging through bootstrapping which allows us to compute confidence estimates for network features in the models we derive.
