## Description

A function to build prediction model using ADAGRAD method.

## Usage

 1 ADAGRAD(dataTrain, alpha = 0.1, maxIter = 10, 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. seed a integer value for static random. Default value is NULL, which means the function will not do static random.

## Details

This function based on SGD with an optimization to create an adaptive learning rate with an approach that accumulate previous cost in each iteration.

## Value

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

## References

J. Duchi, E. Hazan, Y. Singer Adaptive Subgradient Methods for Online Learning and Stochastic Optimization, Journal of Machine Learning Research 12, pp. 2121-2159 (2011)

ADADELTA, RMSPROP, ADAM
  1 2 3 4 5 6 7 8 9 10 11 12 ################################## ## Learning and Build Model with ADAGRAD ## load R Package data data(gradDescentRData) ## get z-factor data dataSet <- gradDescentRData$CompressilbilityFactor ## split dataset splitedDataSet <- splitData(dataSet) ## build model with ADAGRAD ADAGRADmodel <- ADAGRAD(splitedDataSet$dataTrain) #show result print(ADAGRADmodel)