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end{aligned} Clearly, the 'size' of the set (mathcal{F}_{g}) reflects the flexibility of the function g in regard of obtaining expansions of type (1.1).
In completely parametric models as those described here, this dimensionality is directly associated with the notion of degrees of freedom (df), related to the flexibility of the function and the smoothness of the estimated dependency.
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On the other hand, the knot vector determines the local flexibility of the spline function.
The degree and knot vector of B-splines determine the flexibility of the spline function in fitting the observations.
We illustrate the mechanics and the flexibility of the protection function by embedding it in two linear-integer programming models for reserve site selection and applying the models to a case study of Myotis bat conservation on Lopez Island, United States.
Sensitivity to the effect estimates the flexibility of the smooth function of time trends was further tested using df of 8, 10, 15, and 20.
These parameters α and β provide the flexibility of the hazard function to conform to the given data set.
The assumption on the flexibility of the smooth function for excess risks (approach III) was relaxed allowing more degrees of freedom.
Recently, the denoising autoencoder (DAE), one type of DNN, has been shown to be effective in many noise reduction applications because higher-level representations and increased flexibility of the feature mapping function can be learned [30 33].
Recently, DAEs have been shown to be effective in many noise reduction and reverberation suppression applications because higher-level representations and increased flexibility of the feature mapping function can be learned.
Denoising autoencoders (DAEs) have been shown to be effective in many noise reduction applications because higher level representations and increased flexibility of the feature mapping function can be learned [42,43].
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