lmsRegressionLoss

weight vector where the function have to be
  evaluated

matrix of training instances (one instance by
  row)

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

if NULL (which is the case at the first
  call) parameters values are checked

cache

The loss function to perform a least mean square regression

An implementation of "Bundle Methods for Regularized Risk Minimization" (BMRM) inspired by work of Teo et al., JMLR 2010. This universal data mining framework is particularly useful for structured prediction, classification and regression on big data. The package implements L1 and L2 regularization for: linear SVM, multiclass SVM, f-beta optimization, ROC optimization, ordinal regression, quantile regression, epsilon insensitive regression, least mean square, logistic regression, least absolute deviation (see package examples). Other extensions are possible by implementing custom loss functions.

lmsRegressionLoss: The loss function to perform a least mean square regression

Description

Usage

Arguments

Value

References

See Also