An implementation of various learning algorithms based on Gradient Descent for dealing with regression tasks. The variants of gradient descent algorithm are : Mini-Batch Gradient Descent (MBGD), which is an optimization to use training data partially to reduce the computation load. Stochastic Gradient Descent (SGD), which is an optimization to use a random data in learning to reduce the computation load drastically. Stochastic Average Gradient (SAG), which is a SGD-based algorithm to minimize stochastic step to average. Momentum Gradient Descent (MGD), which is an optimization to speed-up gradient descent learning. Accelerated Gradient Descent (AGD), which is an optimization to accelerate gradient descent learning. Adagrad, which is a gradient-descent-based algorithm that accumulate previous cost to do adaptive learning. Adadelta, which is a gradient-descent-based algorithm that use hessian approximation to do adaptive learning. RMSprop, which is a gradient-descent-based algorithm that combine Adagrad and Adadelta adaptive learning ability. Adam, which is a gradient-descent-based algorithm that mean and variance moment to do adaptive learning. Stochastic Variance Reduce Gradient (SVRG), which is an optimization SGD-based algorithm to accelerates the process toward converging by reducing the gradient. Semi Stochastic Gradient Descent (SSGD),which is a SGD-based algorithm that combine GD and SGD to accelerates the process toward converging by choosing one of the gradients at a time. Stochastic Recursive Gradient Algorithm (SARAH), which is an optimization algorithm similarly SVRG to accelerates the process toward converging by accumulated stochastic information. Stochastic Recursive Gradient Algorithm+ (SARAHPlus), which is a SARAH practical variant algorithm to accelerates the process toward converging provides a possibility of earlier termination.

Version: | 3.0 |

Published: | 2018-01-25 |

Author: | Galih Praja Wijaya, Dendi Handian, Imam Fachmi Nasrulloh, Lala Septem Riza, Rani Megasari, Enjun Junaeti |

Maintainer: | Lala Septem Riza <lala.s.riza at upi.edu> |

License: | GPL-2 | GPL-3 | file LICENSE [expanded from: GPL (≥ 2) | file LICENSE] |

URL: | https://github.com/drizzersilverberg/gradDescentR |

NeedsCompilation: | no |

In views: | MachineLearning |

CRAN checks: | gradDescent results |

Reference manual: | gradDescent.pdf |

Package source: | gradDescent_3.0.tar.gz |

Windows binaries: | r-devel: gradDescent_3.0.zip, r-release: gradDescent_3.0.zip, r-oldrel: gradDescent_3.0.zip |

OS X El Capitan binaries: | r-release: gradDescent_3.0.tgz |

OS X Mavericks binaries: | r-oldrel: gradDescent_2.0.1.tgz |

Old sources: | gradDescent archive |

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