LFD Book Forum  

Go Back   LFD Book Forum > Course Discussions > Online LFD course > The Final

Reply
 
Thread Tools Display Modes
  #11  
Old 03-13-2013, 06:09 AM
SeanV SeanV is offline
Junior Member
 
Join Date: Jan 2013
Posts: 8
Default Re: Question 10

Quote:
Originally Posted by yaser View Post
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]
Reply With Quote
Reply

Tags
question 10

Thread Tools
Display Modes

Posting Rules
You may not post new threads
You may not post replies
You may not post attachments
You may not edit your posts

BB code is On
Smilies are On
[IMG] code is On
HTML code is Off

Forum Jump


All times are GMT -7. The time now is 09:54 AM.


Powered by vBulletin® Version 3.8.3
Copyright ©2000 - 2019, Jelsoft Enterprises Ltd.
The contents of this forum are to be used ONLY by readers of the Learning From Data book by Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin, and participants in the Learning From Data MOOC by Yaser S. Abu-Mostafa. No part of these contents is to be communicated or made accessible to ANY other person or entity.