The time complexity of support vector machines (SVMs) prohibits training on huge datasets with millions data points. Recently, multilevel approaches to train SVMs have been developed allow for time-efficient datasets. While regular perform the entire in one—time-consuming—optimization step, first build a hierarchy problems decreasing size that resemble original problem and then an SVM model eac...

In this paper, we propose a Fused Two-Level (FTL) branch predictor combined with an Ahead Calculation method. The FTL predictor is derived from the fusion hybrid predictor. It achieves high accuracy by adopting PAp-base Geometrical History Length (p-GEHL) prediction, which is an effective prediction scheme exploiting local histories. The p-GEHL predictor has several prediction tables indexed fr...