  This study presents a rapid multiple incremental and decremental mechanism
based on Weight-Error Curves (WECs) for support-vector analysis. Recursion-free
computation is proposed for predicting the Lagrangian multipliers of new
samples. This study examines Ridge Support Vector Models, subsequently devising
a recursion-free function derived from WECs. With the proposed function, all
the new Lagrangian multipliers can be computed at once without using any
gradual step sizes. Moreover, such a function relaxes a constraint, where the
increment of new multiple Lagrangian multipliers should be the same in the
previous work, thereby easily satisfying the requirement of KKT conditions. The
proposed mechanism no longer requires typical bookkeeping strategies, which
compute the step size by checking all the training samples in each incremental
round.
