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This scaling ensures that Eq. (15) divided by ω will vary between 0 and 1 for all parameter values thus giving sense to the measure defined by the loss function.
(1) The quality of estimation is measured by the loss function L y, f x, ω)).
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Then we design a loss function to incorporate this preference data with the labeled training data, and learn ranking functions by optimizing the loss function via a derived Ranking SVM framework.
The way of error reduction is arbitrary so that any loss function (loss function represents the inaccuracy of the prediction made by the machine learning algorithm during training, so machine can optimize its performance by minimizing the loss function) can be used depending on the problem being solved.
The parameters of the neutral network are learned by minimizing the loss function with back propagation.
SGD optimises the hyperplane, a multidimensional analogue of a regression line, by minimising the loss function to convergence [66].
On one hand, we want to attain the best classification accuracy by minimizing the loss function.
The predictive model is usually learnt by minimizing the loss function with L2-regularization.
We learn a linear classifier i.e., f(x i )= w T x i + b, by minimizing the loss function given in Eq. 5, using stochastic gradient descent.
Robustness of f with respect to outliers in the target values is achieved by the linear part of the loss function (Figure 1B).
Hence, it is adequate to adaptively select the loss function by investigating whether the corresponding sensor is in the LOS or LOS/NLOS condition.
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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