ADADELTA: ADADELTA Method Learning Function
In gradDescent: Gradient Descent for Regression Tasks

Description Usage Arguments Details Value References See Also Examples

A function to build prediction model using ADADELTA method.

1	ADADELTA(dataTrain, maxIter = 10, momentum = 0.9, seed = NULL)

`dataTrain`	a data.frame that representing training data (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. dataTrain must have at least two columns and ten rows of data that contain only numbers (integer or float).
`maxIter`	the maximal number of iterations.
`momentum`	a float value represent momentum give a constant speed to learning process.
`seed`	a integer value for static random. Default value is NULL, which means the function will not do static random.

This function based on SGD with an optimization to create an adaptive learning rate by hessian approximation correction approach. Correction and has less computation load than ADAGRAD. This method create an exclusive learning rate and doesn't need alpha parameter, but uses momentum parameter same as MGD and AGD.

a vector matrix of theta (coefficient) for linear model.

M. D. Zeiler Adadelta: An Adaptive Learning Rate Method, arXiv: 1212.5701v1, pp. 1-6 (2012)

ADAGRAD, RMSPROP, ADAM

##################################
## Learning and Build Model with ADADELTA
## load R Package data
data(gradDescentRData)
## get z-factor data
dataSet <- gradDescentRData$CompressilbilityFactor
## split dataset
splitedDataSet <- splitData(dataSet)
## build model with ADADELTA
ADADELTAmodel <- ADADELTA(splitedDataSet$dataTrain)
#show result
print(ADADELTAmodel)