ladRegressionLoss

matrix of training instances (one instance by row)

numeric vector of values representing the training labels for each instance in x

The loss function to perform a least absolute deviation regression

Bundle methods for minimization of convex and non-convex risk
under L1 or L2 regularization. Implements the algorithm proposed by Teo et
al. (JMLR 2010) as well as the extension proposed by Do and Artieres (JMLR
2012). The package comes with lot of loss functions for machine learning
which make it powerful for big data analysis. The applications includes:
structured prediction, linear SVM, multi-class SVM, f-beta optimization,
ROC optimization, ordinal regression, quantile regression,
epsilon insensitive regression, least mean square, logistic regression,
least absolute deviation regression (see package examples), etc... all with
L1 and L2 regularization.

ladRegressionLoss: The loss function to perform a least absolute deviation regression

Description

Usage

Arguments

Value

References

See Also