gradDescent (version 3.0)

RMSPROP: ADADELTA Method Learning Function

Description

A function to build prediction model using RMSPROP method.

Usage

RMSPROP(dataTrain, alpha = 0.1, maxIter = 10, momentum = 0.9,
  seed = NULL)

Arguments

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).

alpha

a float value representing learning rate. Default value is 0.1

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.

Value

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

Details

This function based on SGD with an optimization to create an adaptive learning rate by RMS cost and hessian approximation correction approach. In other word, this method combine the ADAGRAD and ADADELTA approaches.

References

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

See Also

ADAGRAD, ADADELTA, ADAM

Examples

# NOT RUN {
##################################
## Learning and Build Model with RMSPROP
## load R Package data
data(gradDescentRData)
## get z-factor data
dataSet <- gradDescentRData$CompressilbilityFactor
## split dataset
splitedDataSet <- splitData(dataSet)
## build model with RMSPROP
RMSPROPmodel <- RMSPROP(splitedDataSet$dataTrain)
#show result
print(RMSPROPmodel)

# }