LSVM {mistral} | R Documentation |
Produce a globally increasing binary classifier built from linear monotonic SVM
LSVM(x, A.model.lsvm, convexity)
x |
a set of points where the class must be estimated. |
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. |
LSVM is a monotonic binary classifier built from linear SVM under the constraint that one of the two classes of data is convex.
An object of class integer
representing the class of x
res |
A vector of -1 or +1. |
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))}) #The convexity is known ## Not run: model.A <- modelLSVM(X, Y, convexity = -1) m <- 10 X.test <- matrix(runif(2*m), nrow = m) classOf.X.test <- LSVM(X.test, model.A, convexity = -1) ## End(Not run)