Matérn kernel functions for support vector regression
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Hello to all,
i have seen that for gauss process regression matern kernel functions are available. But I want to use the Martern kernel function for the support vector regression (SVR). Furthermore I want to adjust the kernel scale parameters automatically with 'OptimizeHyperparameters'.
Is there a possibility or does the martern kernel work in SVR even if it is not listed in the documentation for SVR? And is it than possible to fit the kernel scale parameter here?
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Aditya Patil
2020-12-21
The KernelFunction argument accepts custom function. You can create a function for matern kernel, and pass it to KernelFunction argument.
However, OptimizeHyperparameters only selects one of the inbuilt Kernel functions that are provided. So it's not possible to provide a custom function to OptimizeHyperparameters.
load carsmall.mat
X = [Horsepower,Weight];
Y = MPG;
% This is how you pass custom KernelFunction
mdl = fitrsvm(X, Y, 'KernelFunction', 'myKernel');
% List out the functions you want to test
kernel = optimizableVariable('kernel', {'myKernel', 'myKernel2'}, 'Type', 'categorical');
% Define the function to optimize. Typically this will be
% the loss function on test dataset. For simplicity, I have passed the training data
fun = @(x)loss(fitrsvm(X, Y, 'KernelFunction', char(x.kernel)), X, Y);
% Run bayesian optimization
results = bayesopt(fun, [kernel]);
myKernel and myKernel2 are defined separately. You can define the required functions.
function G = myKernel(U,V)
[m, ~] = size(U);
[n, ~] = size(V);
G = ones([m,n]);
end
And,
function G = myKernel2(U,V)
[m, ~] = size(U);
[n, ~] = size(V);
G = zeros([m,n]);
end
Note that we define multiple kernel functions as kernel function itself does not take arguments except U and V.
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