Name
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. © 2012 IEEE. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/FSKD.2012.6234316 | FSKD |
Keywords | Field | DocType |
classificaton,ensemble classifiers,mapreduce,svm,accuracy,bagging,bootstrapping,classification algorithms,support vector machines,image classification,clustering algorithms,image annotation,algorithm design and analysis,statistical analysis | Annotation,Automatic image annotation,Pattern recognition,Computer science,Bootstrapping,Support vector machine,Artificial intelligence,Contextual image classification,Machine learning,Statistical analysis | Conference |
Volume | Issue | Citations |
null | null | 3 |
PageRank | References | Authors |
0.38 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| nasullah khalid alham | 1 | 104 | 6.80 |
| Maozhen Li | 2 | 1354 | 183.79 |
| yang liu | 3 | 151 | 11.93 |
| Mahesh Ponraj | 4 | 21 | 3.55 |
| Man Qi | 5 | 95 | 15.42 |