Efficient target recognition method for large scale data

An efficient target recognition method for large scale data was proposed, based on self-organizing map neural network and support vector machine. The target data set is divided into clusters by self-organizing map. Then, the support vector machine is applied to classify targets. This method can be used to classify the complex XOR problem and Iris and Appendicitis data. The experimental results show that this method can obtain better recognition results for the complex pattern classification of large scale data, and the training time is shorter than that by the support vector machine method only.