Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.