epsilonInsensitiveRegressionLoss: The loss function to perform a epsilon-insensitive regression (Vapnik et al. 1997)
Description
The loss function to perform a epsilon-insensitive
regression (Vapnik et al. 1997)Usage
epsilonInsensitiveRegressionLoss(w, x, y, epsilon, cache = NULL)
Arguments
w
weight vector where the function have to be
evaluated
x
matrix of training instances (one instance by
row)
y
numeric vector of values representing the
training labels for each instance in x
epsilon
a numeric value setting tolerance of the
epsilon-regression
cache
if NULL (which is the case at the first
call) parameters values are checked
Value
- a 2 element list (value,gradient) where "value" is the
value of the function at point w, and "gradient" is the
gradient of the loss function at w
References
Teo et al. Bundle Methods for Regularized Risk
Minimization JMLR 2010