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Fit SVM with Square Hinge loss function.
SquareHinge(YMAT, DIM = 2L, EPSILON = 1e-05, returnAll = FALSE, rho = 1)
Data. First column is -1 or 1 indicating the class of each observation. The remaining columns are the coordinates of the data points.
Dimension of data. Default value is 2.
Small perturbation value needed in calculation. Default value is 0.00001.
Return all of theta values? Boolean with default value FALSE.
Sensitivity factor to adjust the level of change in the SVM fit when a new observation is added. Default value 1.0
A list containing:
SVM fit parameters.
Number of observation points in YMAT.
Dimension of data.
THETA at each iteration (new point observed) as YMAT is fed into the algorithm one data point at a time.
Intermediate value PSI at each iteration (new point observed).
# NOT RUN { YMAT <- generateSim(10^3,DIM=3) sq1<-SquareHinge(YMAT$YMAT, DIM=3, returnAll=TRUE) # }
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