# Does it matter whether we put regularization parameter ($C$) with error or weight term in Kernel ridge regression?

Kernel ridge regression associate a regularization parameter $$C$$ with weight term ($$\beta$$):

$$\text{Minimize}: {KRR}=C\frac{1}{2} \left \|\beta\right\|^{2} + \frac{1}{2}\sum_{i=1}^{\mathcal{N}}\left\|e_i \right \|_2^{2} \\ \text{Subject to}:\ {\beta^T\phi_i}=y_i - e_i, \text{ }i=1,2,...,\mathcal{N}$$

If we associate $$C$$ with an error term as follows:

$$\text{Minimize}: {KRR}=\frac{1}{2} \left \|\beta\right\|^{2} + C\frac{1}{2}\sum_{i=1}^{\mathcal{N}}\left\|e_i \right \|_2^{2} \\ \text{Subject to}:\ {\beta^T\phi_i}=y_i - e_i, \text{ }i=1,2,...,\mathcal{N}$$

then how this second formulation is different from the first one?

or

Can we associate $$C$$ either with weight term or error term in Kernel ridge regression?

Both formulations lead to the same solution if you correctly choose $$C$$ for both cost functions and if $$C>0$$.

If we have the regularized loss

$$J_1=\dfrac{1}{2}\sum_{n=1}^Ne_n^2+\dfrac{1}{2}C\sum_{k=0}^pw_k^2$$

we will have strong regularization for larger $$C$$ and small regularization for small positive $$C$$.

If we devide the loss $$J_1$$ by the positive $$C$$ we obtain the loss

$$J_2= \dfrac{1}{2C}\sum_{n=1}^Ne_n^2+\dfrac{1}{2}\sum_{k=0}^pw_k^2.$$

As $$C>0$$ we just scaled our loss funtion $$J_1$$, hence the minimum will not change. But the interpretation of $$C$$ will change if we replace it with its inverse as proposed in your question to obtain the loss

$$J_3= \dfrac{1}{2}\tilde{C}\sum_{n=1}^Ne_n^2+\dfrac{1}{2}\sum_{k=0}^pw_k^2.$$

With $$\tilde{C}$$ very small we will have strong regularization. And for large $$\tilde{C}$$ we will have small regularization. That is the reason why we sometimes call $$\tilde{C}$$ the inverse regularization parameter (e.g. see support vector machines).