Description Usage Arguments Value References Examples

softmax loss function may be used to predict probability distributions

1 | ```
softmaxLoss(x, y, loss.weights = 1)
``` |

`x` |
instance matrix, where x(t,) defines the features of instance t |

`y` |
target matrix where y(t,) is a probability distribution that should sum to 1 |

`loss.weights` |
numeric vector of loss weights to incure for each instance of x. Vector length should match nrow(y), but values are recycled if not of identical size. |

a function taking one argument w and computing the loss value and the gradient at point w

Teo et al. Bundle Methods for Regularized Risk Minimization JMLR 2010

1 2 3 4 5 6 | ```
# -- Load the data
x <- cbind(intercept=100,data.matrix(iris[1:4]))
y <- model.matrix(~iris$Species+0)
w <- nrbm(softmaxLoss(x,y))
P <- predict(w,x)
table(max.col(P),iris$Species)
``` |

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