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2
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2
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Is kernalized linear regression parametric or nonparametric?
We know that for linear regression, we can predict:
$$\hat{y} = w^Tx +b$$
Where $w$ is the parameter that minimizes the square loss.
It is easy to prove that for the final solution using gradient ...
- 21
7
votes
1
answer
309
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Kendall-tau and RKHS spaces
Given two random variables $X_1$ and $X_2$, the Kendall-tau correlation coefficient could be defined as $$ \tau(X_{1},X_{2})=\mathbb{P}\Big((X_{1}-\tilde{X}_{1})(X_{2}-\tilde{X}_{2})>0\Big)-\mathbb{...
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