Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/11722
Title: A Resource Aware MapReduce Based Parallel SVM for Large Scale Image Classifications
Authors: Guo, W
Alham, NK
Liu, Y
Li, M
Qi, M
Keywords: Parallel SVM;MapReduce;Image classification and annotation;Load balancing
Issue Date: 2015
Publisher: Springer Verlag
Citation: Neural Processing Letters, pp 1-24, (2015)
Abstract: Machine learning techniques have facilitated image retrieval by automatically classifying and annotating images with keywords. Among them support vector machines (SVMs) are used extensively due to their generalization properties. However, SVM training is notably a computationally intensive process especially when the training dataset is large. This paper presents RASMO, a resource aware MapReduce based parallel SVM algorithm for large scale image classifications which partitions the training data set into smaller subsets and optimizes SVM training in parallel using a cluster of computers. A genetic algorithm based load balancing scheme is designed to optimize the performance of RASMO in heterogeneous computing environments. RASMO is evaluated in both experimental and simulation environments. The results show that the parallel SVM algorithm reduces the training time significantly compared with the sequential SMO algorithm while maintaining a high level of accuracy in classifications.
URI: http://link.springer.com/article/10.1007/s11063-015-9472-z
http://bura.brunel.ac.uk/handle/2438/11722
DOI: http://dx.doi.org/10.1007/s11063-015-9472-z
ISSN: 1370-4621
1573-773X
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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