matlab SVR
时间: 2023-10-31 09:55:26 浏览: 42
SVR, or Support Vector Regression, is a machine learning algorithm used for regression tasks in MATLAB. It is a variant of support vector machines (SVM) that is used for classification problems. SVR aims to find a function that best fits the given data points and maximizes the margin between the predicted values and the target values.
In MATLAB, you can use the `fitrsvm` function to train an SVR model. This function allows you to specify the kernel type, regularization parameters, and other hyperparameters. Once the model is trained, you can use it to predict the outputs for new input data using the `predict` function.
Here is an example of how to use SVR in MATLAB:
```matlab
% Load your data
load data.mat
% Split the data into features and target variables
X = data(:, 1:end-1);
Y = data(:, end);
% Train the SVR model
model = fitrsvm(X, Y, 'KernelFunction', 'gaussian', 'BoxConstraint', 1);
% Predict the outputs for new input data
Xnew = [1, 2, 3; 4, 5, 6; 7, 8, 9];
Ynew = predict(model, Xnew);
```
In this example, I've used a Gaussian kernel function and set the box constraint hyperparameter to 1. You can adjust these values based on your dataset and problem requirements.