Online learning algorithm of direct support vector machine for regression based on Cholesky factorization

With the wide application of support vector machine(SVM), the algorithm of using online learning for realizing regression had been developed furtherly. After the mathematical mode of direct support vector machine (DSVM) for regression was introduced which was of the learning capacity that was similar to least squares support vector machine but less complexity of computation, according to Cholesky factorization, the algorithm of incremental learning and decremental learning were designed for DSVM in this paper, through them online learning that based on time window for regression was realized. Experimental results of simulation through Mackey-Glass chaotic time series and pseudo periodic synthetic time series data set all indicate the feasibility of the learning algorithm which will be beneficial for SVM´s application in depth.