In fact, the hinge loss is the tightest convex upper bound to the 0-1 misclassification loss function,
32.
A benefit of the square loss function is that its structure lends itself to easy cross validation of regularization parameters.
33.
The parameters are estimated by minimizing the loss function for a particular " q "-th quantile:
34.
Note that for an arbitrary loss function V, this approach defines a general class of algorithms named Tikhonov regularization.
35.
Backpropagation uses these error values to calculate the gradient of the loss function with respect to the weights in the network.
36.
Duncan modeled the consequences of two or more means being equal using additive loss functions within and across the pairwise comparisons.
37.
In such scenarios, two simple techniques for convexification are convexification by randomisation and convexification by use of surrogate loss functions.
38.
The problem statement for RLS results from choosing the loss function V in Tikhonov regularization to be the mean squared error:
39.
Popular loss functions include the hinge loss ( for linear SVMs ) and the log loss ( for linear logistic regression ).
40.
The main difference is just that with a loss function, the decision is made by minimizing loss rather than by maximizing utility.
