gaterSVM | R Documentation |
Implementation of Collobert, R., Bengio, S., and Bengio, Y. "A parallel mixture of SVMs for very large scale problems. Neural computation".
gaterSVM( x, y, m, c = 1, max.iter, hidden = 5, learningrate = 0.01, threshold = 0.01, stepmax = 100, seed = NULL, valid.x = NULL, valid.y = NULL, valid.metric = NULL, verbose = FALSE, ... )
x |
the nxp training data matrix. Could be a matrix or an object that can be transformed into a matrix object. |
y |
a response vector for prediction tasks with one value for each of the n rows of |
m |
the number of experts |
c |
a positive constant controlling the upper bound of the number of samples in each subset. |
max.iter |
the number of iterations |
hidden |
the number of neurons on the hidden layer |
learningrate |
the learningrate for the back propagation |
threshold |
neural network stops training once all gradient is below the threshold |
stepmax |
the maximum iteration of the neural network training process |
seed |
the random seed. Set it to |
valid.x |
the mxp validation data matrix. |
valid.y |
if provided, it will be used to calculate the validation score with |
valid.metric |
the metric function for the validation result. By default it is the accuracy for classification. Customized metric is acceptable. |
verbose |
a logical value indicating whether to print information of training. |
... |
other parameters passing to |
expert
a list of svm experts
gater
the trained neural network model
valid.pred
the validation prediction
valid.score
the validation score
valid.metric
the validation metric
time
a list object recording the time consumption for each steps.
data(svmguide1) svmguide1.t = as.matrix(svmguide1[[2]]) svmguide1 = as.matrix(svmguide1[[1]]) gaterSVM.model = gaterSVM(x = svmguide1[,-1], y = svmguide1[,1], hidden = 10, seed = 0, m = 10, max.iter = 1, learningrate = 0.01, threshold = 1, stepmax = 100, valid.x = svmguide1.t[,-1], valid.y = svmguide1.t[,1], verbose = FALSE) table(gaterSVM.model$valid.pred,svmguide1.t[,1]) gaterSVM.model$valid.score
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