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This study presents a rapid multiple incremental and decremental mechanismbased on Weight-Error Curves (WECs) for support-vector analysis. Recursion-freecomputation is proposed for predicting the Lagrangian multipliers of newsamples. This study examines Ridge Support Vector Models, subsequently devisinga recursion-free function derived from WECs. With the proposed function, allthe new Lagrangian multipliers can be computed at once without using anygradual step sizes. Moreover, such a function relaxes a constraint, where theincrement of new multiple Lagrangian multipliers should be the same in theprevious work, thereby easily satisfying the requirement of KKT conditions. Theproposed mechanism no longer requires typical bookkeeping strategies, whichcompute the step size by checking all the training samples in each incrementalround.

Recursion-Free Online Multiple Incremental/Decremental Analysis Based on Ridge Support Vector Learning