【摘要】 支持向量机是一种新型的机器学习方法,该学习方法以结构风险最小化原则取代传统机器学习方法中的经验风险最小化原则,在小样本的机器学习中显示出了优异的性能。将这种新的统计学习方法应用到非线性时间序列预测,并将结果与BP神经网络预测的结果进行比较,结果表明该方法有更高的预测精度。更多还原

【Abstract】 Support Vector Machines(SVM) is a kind of novel learning method,which is based on Structural Risk Minimization principle,unlike traditional machine learning which is based on Empirical Risk Minimization principle.SVM has shown powerful ability in learning with limited samples.This paper applies this new novel statistics learning method to the nonlinear time series forecast,and forecast results are compared with BP Neural Network’s,and it is shown that the presented forecasting method is more accurate.更多还原