I have a question that is about data mining (Which may relate to cross validated), but it's mostly mathematical.
The question is:
Title:
How to fit a function with multi-parameters to data with noise?
Body:
I will give example: My data is: $g(1),g(2),g(3) = 2.1,3.95,8.15$ (Previously being $2$, $4$, $8$)
The function I am trying to fit in is: $h(x) = a^{bx}$. The fit function: $d(x) = (h(x) - g(x))^2$ So to calculate to overall error with my parameters I just do: $f(a,b) = d(1) + d(2) + d(3)$To minimize the overall error I thought just to find $f'(a,b)$, and in this way find when $f(a,b)$ is the closest to $0$, but I don't know. How to find the deviation of a function with multi-parameters? Is there a workaround to this?
Tags:
?? What should I put here?
Does it fit in math SE?