Online Boosting Algorithm Based on Two-Phase SVM Training

Abstract

We describe and analyze a simple and effective two-step online boosting algorithm that allows us to utilize highly effective gradient descent-based methods developed for online SVM training without the need to fine-tune the kernel parameters, and we show its efficiency by several experiments. Our method is similar to AdaBoost in that it trains additional classifiers according to the weights provided by previously trained classifiers, but unlike AdaBoost, we utilize hinge-loss rather than exponential loss and modify algorithm for the online setting, allowing for varying number of classifiers. We show that our theoretical convergence bounds are similar to those of earlier algorithms, while allowing for greater flexibility. Our approach may also easily incorporate additional nonlinearity in form of Mercer kernels, although our experiments show that this is not necessary for most situations. The pre-training of the additional classifiers in our algorithms allows for greater accuracy while reducing the times associated with usual kernel-based approaches. We compare our algorithm to other online training algorithms, and we show, that for most cases with unknown kernel parameters, our algorithm outperforms other algorithms both in runtime and convergence speed.

A Faster Gradient Ascent Learning Algorithm for Nonlinear SVM

“…Also, there have been proposed methods to solve the least squares SVM formulations [7][8][9][10] as well as software packages as SVM light [11], mysvm [12], and many others [3,11,[13][14][15]. It is worth to mention that a series of developments aimed to improve the accuracy of the resulted SVM classifier by combining it with boosting-type techniques [16,17].…”