Using linear programming technique and scaling kernel function, the support vector regression model was obtained.
通过线性规划技术和采用尺度函数作为核函数来实现支持向量回归模型。
Whereas SVM is not suitable for the smoothing regression, a modified support vector regression model is proposed.
鉴于后者有着对于光顺性的特殊要求，已有的支持向量机并不适用。
The generalization of the support vector regression model, the optimization of the generalization capacity, and the training speed are discussed.
同时对广泛的支持向量回归模型、优化支持向量模型的泛化能力和运算速度等方面进行讨论。
Online identification algorithm of support vector regression is used to build the inverse model for the plant.
采用支持向量回归在线辨识算法作为建模方法建立被控对象的逆模型。
A support vector regression method based on classification is presented to solve the nonlinear regression problem with unknown data distribution and mathematical model.
提出了一种基于分类技术的支持向量回归方法，解决数据分布未知、数学模型未知的非线性回归问题。
Proposed the model of fuzzy chance constrained programming with fuzzy decision, and did some research on fuzzy linear support vector regression (algorithm) on this base.
给出带有模糊决策的模糊机会约束规划模型，在此基础上，研究模糊线性支持向量分类机(算法)和模糊线性支持向量回归机(算法)。
Parameter tuning of Support Vector Regression (SVR) has been a critical task to develop a SVR model with good generalization performance.
在回归支持向量机的建模中，参数调节问题一直是影响模型性能的重要因素之一。
The support vector approach learns a parsimonious regression model from the given data to avoid the data over-fitting problem.
支持矢量回归方法可以在给定的资料中生成一个简洁的回归模式，以避免常规机器学习法中的资料过度学习问题。
Then, by the function expansion, the nonlinear transfer function of this model was converted to intermediate model which is linear one and can be identified using support vector regression (SVR).
再利用函数展开将模型的非线性传递函数转换为等价的线性中间模型，并通过SVR求取中间模型参数。
Then, by the function expansion, the nonlinear transfer function of this model was converted to intermediate model which is linear one and can be identified using support vector regression (SVR).
再利用函数展开将模型的非线性传递函数转换为等价的线性中间模型，并通过SVR求取中间模型参数。
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