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

Th is study presents a rapid multiple incremental and decremental mechanism ba sed on Weight - Error Curves (WECs) fo r support - vector a nalysi s . To ha ndle rapidly increas ing amounts of data, recursion - free computation is proposed for predicting the Lagrangian multipliers of new samples . This study examines the characteristics of Ridge S upport V ector M odels, including Ridge S upport V ector Machines and Regression, subsequently devis ing a recursion - free function derived from WECs . With this proposed function, a ll of the new Lagrang ian multipliers can be computed at once without using any gradual step sizes. Moreover, such a function can relax a constraint, where the increment of new multiple Lagrang ian multipliers should be the same in the previous work, thereby easily satisfying the requirement of Karush - Kuhn - Tucker (KKT) conditions . The proposed mechanism no longer requires t ypical time - consuming bookkeeping strategies, which compute the step size by checking all the training samples in each incremental round. Experiments were carried out on open datasets for evaluating our work. The results showed that the computation al speed was successfully enhanced, better than the baselines. Besides, the accuracy still remained. These findings revealed that the proposed method was appropriate for incremental/decremental learning, thereby demonstrating the effectiveness of the propose d idea.