一种新的支持向量回归算法及其在集装箱吞吐量预测中的应用

支持向量机是基于统计学习理论框架下的一种新的通用机器学习方法,是一种处理非线性分类和非线性回归的有效方法。由于具有完备的理论基础和出色的学习性能,该技术已成为当前国际机器学习界的研究热点,能较好地对应解决小样本、高维数、非线性和局部极小点等实际问题。近来,SVR方法被引入求解回归和预测问题,并在各领域中得到广泛的应用。文章提出了一种新的基于单参数的Lagrangian支持向量回归算法,并将该算法应用在集装箱吞吐量预测中。估算结果证明了这种改进的支持向量回归算法在集装箱吞吐量预测中的有效性和实用性。

Prediction of Container Throughput Using a Single-parameter Lagrangian Support Vector Regression

The Support Vector Machines(SVM),a new general machine learning method based on the frame of statistical learning theory,is an effective method for processing the non-liner classification and regression.Because of its solid theoretical background and excellent generalization performance,it has become the hotspot of machine learning.This method can solve those practical problems such as limited samples,high dimension,non-linear problem and local minimum.Recently,Support Vector Regression(SVR) has been introduced to solve regression and prediction problems and widely used in many fields.A new algorithm of Support Vector Regression is proposed in this article which is named Single-parameter Lagrangian Support Vector Regression,and it is used in the prediction of container throughput in Shanghai Port.The results of experiments reveal the practicability and effectiveness of this algorithm in prediction of container throughput.