updateLSVM {mistral} | R Documentation |
Update the existing classifier LSVM with a new set of data.
updateLSVM(X.new, Y.new, X, Y, A.model.lsvm, convexity, PLOTSVM = FALSE, step.plot.LSVM = 1, hyperplanes = FALSE, limit.state.estimate = TRUE)
X.new |
a matrix containing a new data sets |
Y.new |
a vector containing -1 or +1 that reprensents the class of each elements of X.new. |
X |
a matrix containing the data sets |
Y |
a vector containing -1 or +1 that reprensents the class of each elements of X. |
A.model.lsvm |
a matrix containing the parameters of all hyperplanes. |
convexity |
Either -1 if the set of data associated to the label "-1" is convex or +1 otherwise. |
PLOTSVM |
A boolean. If TRUE, plot the data. |
step.plot.LSVM |
A plot is made each |
hyperplanes |
A boolean. If TRUE, plot the hyperplanes obtained. |
limit.state.estimate |
A boolean. If TRUE, plot the estimate of the limit state. |
updateLSVM allows to make an update of the classifier LSVM.
An object of class matrix
containing the parameters of a set of hyperplanes
The argument PLOTSVM is useful only in dimension 2.
Vincent Moutoussamy
R.T. Rockafellar:
Convex analysis
Princeton university press, 2015.
N. Bousquet, T. Klein and V. Moutoussamy :
Approximation of limit state surfaces in monotonic Monte Carlo settings
Submitted .
# A limit state function f <- function(x){ sqrt(sum(x^2)) - sqrt(2)/2 } # Creation of the data sets n <- 200 X <- matrix(runif(2*n), nrow = n) Y <- apply(X, MARGIN = 1, function(w){sign(f(w))}) ## Not run: model.A <- modelLSVM(X,Y, convexity = -1) M <- 20 X.new <- matrix(runif(2*M), nrow = M) Y.new <- apply(X.new, MARGIN = 1, function(w){ sign(f(w))}) X.new.S <- X.new[which(Y.new > 0), ] Y.new.S <- Y.new[which(Y.new > 0)] model.A.new <- updateLSVM(X.new.S, Y.new.S, X, Y, model.A, convexity = -1, PLOTSVM = TRUE, step.plot.LSVM = 5) ## End(Not run)