The StatLSSVM toolbox is written so that only a few lines of code are necessary in order to perform standard nonparametric regression, regression with correlated errors and robust regression. In addition, construction of additive models and pointwise or uniform confidence intervals are also supported. A number of tuning criteria such as classical cross-validation, robust cross-validation and cross-validation for correlated errors are available. Also, minimization of the previous criteria is available without any user interaction.

latticeseq_b2.m
fossil.mat
fminsearchbnd.m
faithful.mat
diabetes.mat
leaveoneout.m
crossval2lp1.m
crossval.m
densitylssvm.m
csa.m
simplex.m
cilssvm.m
rcrossval.m
densitylssvm2d.m
tunelssvm.m
bimodNW.m
initlssvm.m
rsimplex.m
tbform.m
kernel_matrix.m
kernel_matrix2.m
gcrossval.m
epdfhist.m
plotlssvmadd.m
weightingscheme.m
bitreverse32.m
changelssvm.m
cvl.m
hall.m
huber.m
linf.m
lscvhist.m
lssvmMATLAB.m
mae.m
mse.m
plotlssvm.m
regdata2d.m
robustlssvm.m
simlssvm.m
smootherlssvm.m
trainlssvm.m
nba2.mat
logo.pdf
beluga.mat
birth.mat
UStemp.mat
statgetargs.m
progress.m
nba.mat
lidar.mat

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