varianceDescaling

a data.frame that representing dataset (\(m \times n\)),
where \(m\) is the number of instances and \(n\) is the number
of variables where the last column is the output variable. dataSet
must have at leas two columns and ten rows of data that contain
only numbers (integer or float).

dataSet

a matrix that has value of variance scaling
parameter, such as mean value and standard deviation value of
data that can be used to restore the original value of dataset.
This parameter is exclusively produced by <code><a rd-options="" href="/link/varianceScaling?package=gradDescent&version=3.0" data-mini-rdoc="gradDescent::varianceScaling">varianceScaling</a></code>
function.

varianceParameter

A function to revert the value that has been done by variance/ .
standardization scaling method.

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.

Lala Septem Riza

gradDescent

Gradient Descent for Regression Tasks

varianceDescaling function

a matrix that has value of variance scaling
parameter, such as mean value and standard deviation value of
data that can be used to restore the original value of dataset.
This parameter is exclusively produced by <code><a rd-options='' href='varianceScaling'>varianceScaling</a></code>
function.

varianceDescaling: Variance/Standardization Revert Function

Description

Usage

Arguments

Value

Details

See Also

Examples