A Data Fusion Approach Based on Parallel Support Vector Machine

Support vector machine has some advantages, such as simple structure and good generalization, which is one implementation in statistical learning theory. SVM offers a kind of effective way for the data fusion problem of little sample, non-linear and high dimension. In this paper, mobile agents are applied to data fusion system. The model and the study method of data fusion system are improved. An approach of data fusion based on SVM is proposed. The experiment results show that this hierarchical and parallel SVM training algorithm is efficient to deal with large-scale classification problems and has more satisfying accuracy in classification precision.