Description Usage Arguments Details Examples

RCANE

Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function. bgd (Batch Gradient Descent) - Batch Gradient Descent updates the parameters by computing loss function of the entire dataset. sgd (Stochastic Gradient Descent) - Stochastic Gradient Descent updates the parametes by computing loss function for each record in the dataset. cd (Coordinate Descent) - Coordinate Descent updates the parameter by minimizing the loss function along each coordinate axis. mini-bgd (Mini Batch Gradient Descent) - Mini Batch Gradient Descent divides the data into batches and updates the parameters by computing the loss function for each batch.

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`formula` |
an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted. |

`data` |
an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which lm is called. |

`method` |
the method to be used. Possible values include "bgd", "sgd", "cd" and "mini-bgd". |

`alpha` |
the learning rate - typically this would be set to the optimum value |

`max.iter` |
the maximum number of iterations - in case of delayed convergence, the function would terminate after max.iter iterations |

`precision` |
the precision of the result |

`boldDriver` |
set |

`AdaGrad` |
set |

`...` |
additional arguments to be passed to the low level regression fitting functions. |

`rlm`

is an interface for the optimization functions written in the rcane project.

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