How to integrate customized kernel function into "regression learner" toolbox?
Show older comments
The regression learner in 2017 contains SVM method with linear, rbf, and poly kernels. What if I want to use my own kernel? Is there a way that I can integrate my own kernel to the toolbox so that I can use it easily?
Accepted Answer
Hello,
The "Regression Learner" app does not currently support specifying custom Kernel functions. However, if you are willing to consider a command-line approach, you could use the fitrsvm function to fit a Regression Support Vector Machine. In the inputs for fitrsvm, you can specify the "KernelFunction" Name-Value pair to point to your custom Kernel function. Please refer the following link for more information:
As an alternate workaround, you can generate code for your regression model from the Regression Learner App using the "Export Model" drop down. You can modify the "KernelFunction" property in the generated code to point to your custom Kernel function. You might have to also modify other Kernel associated parameters such as "KernelScale" accordingly as required by your use case.
On a different note, I work for the MathWorks and I have forwarded this use case to the appropriate product team.
2 Comments
xi
on 24 Apr 2017
zhaorong huang
on 13 Aug 2023
It's already 2023. Is there a more detailed instance of the calling code available for use
More Answers (1)
This has been implemented in the 2023b version (or earlier):
Categories
Find more on Support Vector Machine Regression in Help Center and File Exchange
