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

TL;DR

This paper introduces a fast, recursion-free method for online support vector analysis that efficiently updates multiple Lagrangian multipliers during incremental or decremental learning without iterative step size adjustments.

Contribution

It proposes a novel recursion-free function based on Weight-Error Curves for Ridge Support Vector Models, enabling simultaneous computation of all new Lagrangian multipliers and relaxing previous constraints.

Findings

Enables rapid updates of support vector models in online learning scenarios.

Eliminates the need for iterative step size calculations and bookkeeping strategies.

Ensures compliance with KKT conditions during incremental updates.

Abstract

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.