Hi,
I’m trying to understand what RBFs Surrogates.jl is using. For example, the documentation only specifies that it uses RadialBasis . The [code] specifies that there is a linearRadial and cubicRadial. What equation is used as the surrogate?
In the literature, there are also multiple definitions for RBFs:
Wikipedia only lists the Kernel, f.ex. exp(-r^2)
SMT Describes a sum of RBFs with a polynomial term.
Another write-up Drops the polynomial term.
Surrogates.jl is a high level package based on Gaussian Process.
Gaussian Process suppose your data sampling from a MVNormal,so params just mean vector and covariance matrix.in GP these called mean function and covariance function .
when we use data to build covariance matrix ,we can map data from data space to feature space or empirical space.
Why we need map data to higher dimension space? Maybe our observing data just some higher dimension data embedding in our real word. you can think there are some latent data generate machines(in higher dimension) to produce our data, and these machine defining by mean vector and covariance matrix.
so we need mathematics methods to search these data generate machine’s params
kernel function or kernel tricks are function to mapping data to feature space or empirical space.
different kernel function mapping data to different feature space. That’s point!
In short ,it’s powerful, statistical and mathematical.please reference
It doesn’t seem to be documented, but the RadialBasis constructor takes a keyword argument rad to specify the type of radial-basis function, which defaults to linearRadial: what Wikipedia calls a “polyharmonic spline” of degree 1. You could easily define your own surrogate based on the examples in Surrogates.jl.
(This answer doesn’t make any sense.)
Whomever that is might’ve just used ChatGPT haha. Yeah what the heck is that one.
We should document this part though. @rkube could you please open an issue so it gets tracked? Thanks.
Sure