Description Usage Arguments Value Author(s) See Also Examples

The Suport Vector Regression (SVR) employs epsilon-intensive loss which ignores errors smaller than epsilon. This algorithm computes the entire paths for SVR solution as a function of `epsilon`

at a given regularization parameter `lambda`

, which we call `epsilon`

path.

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`x` |
The data matrix (n x p) with n rows (observations) on p variables (columns) |

`y` |
The real number valued response variable |

`lambda` |
The regularization parameter value. |

`kernel.function` |
User defined kernel function. See |

`param.kernel` |
Parameter(s) of the kernels. See |

`ridge` |
Sometimes the algorithm encounters singularities; in this case a small value of ridge can help, default is |

`eps` |
A small machine number which is used to identify minimal step sizes |

`eps.min` |
The smallest value of epsilon for termination of the algorithm. Default is |

`...` |
Generic compatibility |

An 'epspath' object is returned.

Do Hyun Kim, Seung Jun Shin

`predict.epspath`

, `plot.epspath`

, `svrpath`

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