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In general, climate change affects runoff not only by changing the hydrological inputs (precipitation and potential evaporation) but also by altering the watershed characteristics as represented by the parameter w; while the impacts of human activities on runoff are exerted mainly through the alteration of the watershed characteristics.
The representation of the EM objects is governed by the parameter w, which determines the extension of the surface layer of the object and its outer shape.
In our model, the preferences of physicians for urban areas are reflected by the parameter w.
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We can modulate the effect of the prior energy by setting the parameter w properly.
The change of the parameter W by the CD training is given by begin{aligned} Delta W_{ij}=epsilon (langle v_ih_j rangle _{mathrm{data}}-langle v_ih_j rangle _{mathrm{recon}}).
For example, during iteration n we update the parameter w s by using the following formula: (9) w s, n + 1 = w s, n − ∂ E (L L b ) ∂ w + ∂ E (L L d ) ∂ w ∂ 2 E (L L b ) ∂ 2 w + ∂ 2 E (L L d ) ∂ 2 w All the parameters are iteratively updated until the EM algorithm converges.
Here, the parameter w is estimated by using a grid of values, i.e. w = 0.01, 0.03,..., 0.19, and computing the REML log-likelihood for each value.
The hyperplane is represented by the parameters (w,b) of Eq. (5) which are learned by a mistake-driven algorithm conducting incremental updates from a stream of instances.
The parameter w is a weighting factor.
The parameter W is unknown beforehand in the above procedure.
The parameter w controls the intensity of selection.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com