Abstract:

The pruning algorithms for sparse least squares support
vector regression machine are common methods,and easily com-
prehensible,but the computational burden in the training phase
is heavy due to the retraining in performing the pruning process,
which is not favorable for their applications.To this end,an im-
proved scheme is proposed to accelerate sparse least squares
support vector regression machine.A major advantage of this
new scheme is based on the iterative methodology,which uses
the previous training results instead of retraining,and its feasibility
is strictly verified theoretically.Finally,experiments on bench-
mark data sets corroborate a significant saving of the training time
with the same number of support vectors and predictive accuracy
compared with the original pruning algorithms,and this speedup
scheme is also extended to classification problem.