用于回归估计的支持向量机方法

用于回归估计的支持向量机方法以可控制的精度逼近非线性函数，具有全局最优、良好泛化能力等优越性能，得到广泛的研究。描述了该方法的基本思想，着重讨论了V-SVM、最小二乘SVM、加权SVM、线性SVM等支持向量机的新方法，降低训练时间和减少计算复杂性的分解法、SMO及增量学习算法。在非线性系统参数辨识、预测预报、建模与控制研究中，支持向量机是很有发展前途的研究方法。

Support Vector Machines for Regression

Support Vector Machine (SVM) for regression has recently attracted growing research interest due to its obvious advantage such as nonlinear function approximation with arbitrary accuracy, and good generalization ability, unique and globally optimal solutions. An overview of the basic ideas underlying SVM for regression is given in this paper. In particular, new methods such as n-SVM, LS-SVM, weighted SVM and linear SVM, and optimization algorithms including decomposition method and SMO and incremental learning with fast computational speed and ease of implementation are concentrated as well. SVM for regression is an important and promising new direction in the area of nonlinear parameter identification, forecast, modeling and control.