Question 14 - bias term
 

For Question 15 we implement the RBF model, and there is a bias term to be calculated. It seems to me that there are two ways of going about this:

1) use a similar logic to what we had for linear regression. This seems to be what is suggested on slide 15 of lecture 16 (RBF network slide).

2) in the RBF notes it's mentioned in the footnote on page 27 that the bias = mean(y).

I implemented both and get different values for the bias as well as for Eout. I was wondering if anyone had tried both methods and come up with consistent values (i.e., I have a bug that I haven't managed to find despite looking hard and long) or if there is a reason to pick one method over the other.:clueless:

Also while I'm at it, shouldn't the last line of the footnote only refer to w since the centers are chosen in an unsupervised way?

Re: Question 15 - bias term
 

This is the correct way. Treat as you treated in linear regression. The only reason it's called here is not to conflict with the SVM kernel version.

Re: Question 15 - bias term
 

TTT (To The Top) Useful to know :)

Re: Question 15 - bias term
 

What is the bias term in a linear regression? Aren't they solved as the pseudo inverse: inv(x'*x)*x'*y?

Re: Question 15 - bias term
 

It is in our notation, corresponding to which is the first column of the matrix that is used in the pseudo-inverse.