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## Classifying with the kernel K

Hello! I can't understand the passages associated to the topic "Classifying with the kernel K" in lecture 7b notes.

We start saying that

y = φ(x)^T w

and considering the representer theorem we arrive at the expression

φ(x)^T φ(X)^T α

but I would have written

φ(x)^T X^T α

Is that right? Are the two expressions equivalent? Because, if they are, I can't see why.

Thanks for your answer!

I think that when you do feature augmentation, you have always to consider φ(X), the augmented matrix, and not X, even for the prediction. It wouldn't make sense to augment a datapoint and then use the matrix X to predict, and the dimensions in most of the cases wouldn't even match.

## 2

I agree, I would say the same, if you learn parameters in the augmented space, since the kernel is non-linear, you cannot relate them to the original vector in a linear relationship (here matrix multiplication)

Can you explain a bit more why you would have written φ(x)^T X^T α?

I was just applying the result of the representer theorem, saying that w = X^T α.

Hence, φ(x)^T w = φ(x)^T X^T α.

But what the others said completely makes sense. Thanks everyone!

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