Support vector machines (SVM) are a kind of novel machine learning methods, based on statistical learning theory, which have been developed for solving classification and regression problems.
支持向量机是一种基于统计学习理论的新颖的机器学习方法，该方法已广泛用于解决分类和回归问题。
Bayesian methods are those that are explicitly apply Bayes' Theorem for problems such as classification and regression.
贝叶斯方法是那些明确地在分类和回归问题中应用贝叶斯定理的算法。
These are two kinds of difficult problems in Nonlinear Regression Analysis for their complexities and flexibilities in proceeding by hand without normal method to abide by.
这一点也是目前非线性回归分析中灵活性较高、人为因素复杂且无一般程式可供遵循的难点问题之一。
The Support Vector Regression(SVR)is used for the time series analysis and prediction to resolve the complex nonlinear system modeling problems.
用支持向量回归（SVR）的方法分析和预测时间序列，可解决复杂非线性系统的建模问题。
The Support Vector Regression(SVR)is used for the time series analysis and prediction to resolve the complex nonlinear system modeling problems.
用支持向量回归（SVR）的方法分析和预测时间序列，可解决复杂非线性系统的建模问题。
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