支持向量机是一种基于统计学习理论的机器学习方法，可以分析数据，识别模式

Support vector machine is a machine learning method based on statistical learning theory, which can analyze data, identify patterns, and use it for classification and regression. At the same time, support vector machine requires less training samples and has strong generalization ability. Support vector regression mainly realizes linear regression by constructing a linear decision function in a high-dimensional space after dimensional increase. In order to adapt to the nonlinearity of the training sample set, the traditional fitting method usually adds a higher-order term after the linear equation, which is easy to use. There is an overfitting problem.

Support vector machine is a machine learning method based on statistical learning theory. It can analyze data, recognize patterns, and be used for classification and regression. At the same time, support vector machine needs less training samples and has strong generalization ability. Support vector regression mainly realizes linear regression by constructing linear decision function in high-dimensional space after dimension upgrading. In order to adapt to the nonlinearity of training sample set, the traditional fitting method usually adds high-order terms after the linear equation, which is easy to produce the problem of over fitting.

Support vector machine is a machine learning method based on statistical learning theory, which can analyze data and identify patterns for classification and regression. At the same time, support vector machine needs fewer training samples and has strong generalization ability. Support vector regression (SVR) mainly realizes linear regression by constructing linear decision function in high-dimensional space after dimension elevation. In order to adapt to the nonlinearity of training sample set, the traditional fitting method usually adds higher-order term after linear equation, which is prone to over-fitting.