The parameter selection method for support
vector regression based on adaptive fusion of
mixed kernel function
Initialization:
() For original data set , select the mixed
kernel function, and set the initial parameter
state value .
() Divide into groups by using fold
cross validation method denoted by .
While (Parameter state value does not meet the
set conditions) do
Time update process:
() Calculate weights , , , using
formulas (6)-(7).
Measurement update process:
() Decompose one step prediction error
covariance matrix and evaluate the
cubature point according to formula () in
reference [19].
() Train the data set based on the LIBSVM
algorithm to obtain the final prediction output.
() Combining predict , compute one step
prediction by using formula (12).
() Use formula ()–() of reference [19] to
implement the subsequent measurement update.
End while
End
Algorithm 1: The detailed algorithm steps of the parameter selection method for support vector regression based on adaptive fusion of mixed kernel function.