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Machine learning uses statistics techniques to construct a model from observed data.
The format of questions depends on the degree of freedom given to the experts: should they construct a model from scratch, position a set of existing variables and draw the linkages between them, or adapt or evaluate existing model(s)?
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The algorithm constructs a model from the detected moving object and match the model with successive image frames to track the target object.
However, in nonlinear contexts, where one might be constructing a model from a data set generated by observing a system, there are potentially many nonlinear models that can be constructed, where each model is as empirically adequate to the system behavior as any other.
Bottom-up modelling, in contrast, involves constructing a model from well-defined, fine-grained components.
Rather than constructing a model from scratch, we rely on these complementary modeling works by adopting all their model assumptions, equations and parameter settings.
Once the U O shells had been satisfactorily resolved, we then constructed a model from a single Fe shell to the full model including four Fe shells refining the model each time and assessing the statistical relevance of each additional shell by way of an F-test (Supporting Information Table SI-7).
Here, we construct a model based on the load data from 2001 to 2012, and conduct virtual forecasting from 2013 to 2015 to test its effectiveness.
Instructions are provided for constructing a model of chromatin from three rubber surgical tubes to illustrate its tight coiling.
We constructed a model estimating WC from BMI and three demographic variables — gender, age, and race/ethnicity — because WC is increasingly recognized as a more accurate and therefore more useful metric than BMI alone for predicting cardiometabolic risk.
Global modeling constructs a model which is independent from user queries.
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