Fast incremental weighted support vector machines for predicating stock index
快速增量加权支持向量机预测证券指数

Traditional support vector machine (SVM) is effective only for small size of samples.When the size of sample is large, it exhibits a low training speed and a large required memory. Thus, it is not suitable for increment learning. Furthermore, traditional increment learning algorithms such as neural network have local minima only. To tackle this problem, a fast incremental weighted support vector machines for predicting the stock index is put forward. The algorithm model reconstructs the phase for the index, and then decomposes the sample space into subsets and gives different weights to them. Experimental results show that modified algorithm raises the training speed while maintaining the same precision.