ModelSV {svcR} | R Documentation |
Computes kernel matrix, lagrange coefficients, support vectors and radius
## S4 method for signature 'numeric': ModelSV.compute(x , MatriceKernel = NULL, MatriceK = NULL, Nu = 1, nlin = 1, MaxIter = 2, MaxValA = 2, AroundNull = 0.01, AroundNullVA = 0.01 ) ## S4 method for signature 'matrix': OptimQuadProgWcluster(MatriceKernel , Nu = 1, MaxValA = 2, MinW = 0.0001 )
x |
method of lagrange computation 1 (stochastic) or 2 (quadratic) |
MatriceKernel |
kernel matrix with vector format |
MatriceK |
kernel matrix with vector format |
Nu |
svc parameter |
nlin |
number |
MaxIter |
maximum iteration for coefficients computation |
MaxValA |
number of neigbours on the grid |
AroundNull |
almost null parameter |
AroundNullVA |
almost null parameter for coefficients |
MinW |
min value for coefficients |
An S4 object of class ModelSV
The object is the svc model along with
the slots :
lagrangeCoeff |
lagrange coefficients : VectorsYA$A |
RadiusC |
radius of the hypersphere containing data |
SmallR |
residu of radius |
OptimQuadProgWcluster return a list containing lagrange coefficients.
slots can not be accessed.
Nicolas Turenne - INRA France nicolas.turenne@jouy.inra.fr
N.Turenne , Some Heuristics to speed-up Support Vector Clustering , technical report 2006, INRA, France http://migale.jouy.inra.fr/~turenne/svc.pdf
## exemple with iris data # model computing fmc = findSvcModel.Test(); L = getlagrangeCoeff(fmc); # catch lagrange coefficients L$A; # lagrange coefficients computed in the model