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-   -   Question 10 (http://book.caltech.edu/bookforum/showthread.php?t=1463)

SeanV 03-13-2013 07:09 AM

Re: Question 10
 
Quote:

Originally Posted by yaser (Post 9906)
Correct. The solution was also given in slide 11 of Lecture 12 (regularization).

yes my point was how do you solve this numerically - given that people will already have a good least squares code ( doing SVD on Z to avoid numerical ill conditioning), there is no need to implement (poorly) a new regularised least squares solver

you can just add a few data points at the end of your training data and feed it into your least squares solver. ie
\lambda |w|^2 = \sum_i (y_i-\sqrt(lambda)w_i)^2

ie if w is d dimensional you append to your Z matrix the additional matrix=sqrt(lambda)*eye(d) and append a d vector of zeros to your y

(eye(d) is d by d identity matrix) [ but this is much better explained in the notes i linked to]


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