- In case one specifies the parameter to be 'auto', the software selects an appropriate scale factor using a heuristic procedure. This heuristic procedure uses subsampling, so estimates can vary from one call to another. Therefore, to reproduce results, set a random number seed using rng before training.
- If one specifies KernelScale and a custom kernel function, for example, 'KernelFunction', 'kernel', then the software throws an error. Then scaling must be applied within kernel.
SVM KernelFunction and KernelScale. What is the difference?
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When using Support Vector Machines in the Classification Learner, what is the diffence between the KernelFunction (gaussian, linear, quadratic or cubic) and the KernelScale (which could be any number)?
Is a SVM with a linear KernelFunction and Kernelscale of 2 the same as a SVM wth a quadratic KernelFunction and a KernelScale of 1?
Thanks in advance.
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Pratyush Roy
el 5 de Feb. de 2021
Hi Dylan,
The KernelScale is a scaling parameter which is used to scale the data before evaluation of the appropriate Gram matrix.
The KernelFunction is the function used to compute the elements of Gram Matrix G after scaling has been applied using the KernelScale parameter.
The linear kernel function can be defined as:
Scaling the predictor variables by a factor of, say, s gives us the Gram matrix
This is not same as a quadratic kernel function with scaling factor 1 with the following form:
Hope this helps!
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