A distributed SVM ensemble for image classification and annotation
Nasullah Khalid Alham, Maozhen Li, Yang Liu, Mahesh Ponraj, Man Qi · 2012
Combination of classifiers leads to a substantial reduction of classification errors in a wide range of applications. Among them SVM ensembles with bagging have shown better performance in classification than a single SVM. However, the training process of SVM ensembles is notably computationally intensive especially when the number of replicated training datasets is large. This paper presents MRESVM, a MapReduce based distributed SVM ensemble algorithm for image annotation which re-samples the training dataset based on bootstrapping and trains SVM on each dataset in parallel using a cluster of computers. MRESVM is evaluated in a experimental environment and the results show that the MRESVM algorithm reduces the training time significantly while achieves high level of accuracy in classifications.