Last data update: 2014.03.03
|
R: lasvmTrain
lasvmTrain | R Documentation |
lasvmTrain
Description
Use lasvm to train a given problem.
Usage
lasvmTrain(x, y, gamma = 1, cost = 1, degree = 3, coef0 = 0,
optimizer = 1, kernel = 2, selection = 0, termination = 0,
sample = 1e+08, cachesize = 256, bias = 1, epochs = 1,
epsilon = 0.001, verbose = FALSE)
Arguments
x |
data matrix
|
y |
labels
|
gamma |
RBF kernel parameter
|
cost |
regularization parameter
|
degree |
degree for poly kernel
|
coef0 |
coefficient for poly kernel
|
optimizer |
type of optimizer
|
kernel |
kernel type
|
selection |
selection strategy
|
termination |
criterion for stopping
|
sample |
time for stopping/number of iterations tec
|
cachesize |
size of kernel cache
|
bias |
use bias?
|
epochs |
number of epochs
|
epsilon |
stopping criterion parameter
|
verbose |
verbose output?
|
Value
a list consisting of
alpha alpha for SVs as vector
SV support vectors as matrix
Examples
model = lasvmR::lasvmTrain (x = as.matrix(iris[seq(1,150,2),1:4]),
y = (as.numeric(iris[seq(1,150,2),5]) %% 2)*2-1,
gamma = 1,
cost = 1,
kernel = 2)
ytrue = (as.numeric(iris[seq(2,150,2),5]) %% 2)*2-1
result = lasvmPredict (x = as.matrix(iris[seq(2,150,2),1:4]), model)
ypred = result$predictions
error = sum(abs(ypred - ytrue))/length(ytrue)
cat ("Error rate =", error*100)
Results
|