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支持向量机回归

用于回归模型的支持向量机

为了提高在中低维数据集上的准确度,可以使用 fitrsvm 训练支持向量机 (SVM) 模型。

为了减少在高维数据集上的计算时间,可以使用 fitrlinear 高效地训练线性回归模型,例如线性 SVM 模型。

App

回归学习器Train regression models to predict data using supervised machine learning

模块

RegressionSVM PredictPredict responses using support vector machine (SVM) regression model

函数

全部展开

fitrsvmFit a support vector machine regression model
predictPredict responses using support vector machine regression model
fitrlinearFit linear regression model to high-dimensional data
predictPredict response of linear regression model
fitrkernelFit Gaussian kernel regression model using random feature expansion
lossRegression loss for Gaussian kernel regression model
predictPredict responses for Gaussian kernel regression model
resumeResume training of Gaussian kernel regression model
crossvalCross-validated support vector machine regression model
partialDependenceCompute partial dependence
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
limeLocal interpretable model-agnostic explanations (LIME)
shapleyShapley values

全部展开

RegressionSVMSupport vector machine regression model
CompactRegressionSVMCompact support vector machine regression model
RegressionLinearLinear regression model for high-dimensional data
RegressionPartitionedLinearCross-validated linear regression model for high-dimensional data
RegressionKernelGaussian kernel regression model using random feature expansion
RegressionPartitionedKernelCross-validated kernel model for regression

主题

Predict Responses Using RegressionSVM Predict Block

Train a support vector machine (SVM) regression model using the Regression Learner app, and then use the RegressionSVM Predict block for response prediction.

Understanding Support Vector Machine Regression

Understand the mathematical formulation of linear and nonlinear SVM regression problems and solver algorithms.