Kernel Logistic Regression

We can kernelize logistic regression just like other linear models by observing that the parameter vector $\bold w$ is a linear combination of the feature vectors $\Phi(X)$, that is:

$$\bold w = \Phi(X) \bold a$$

Here, $\bold a$ is the dual parameter vector, and, in this case, the loss function now depends upon $\bold a$ ...