LFD Book Forum question on 2.8 and 2.9
 User Name Remember Me? Password
 Register FAQ Calendar Mark Forums Read

 Thread Tools Display Modes
#1
07-19-2012, 02:42 PM
 yijun2011@yahoo.com Junior Member Join Date: Jul 2012 Posts: 3
question on 2.8 and 2.9

Could someone please help to clarify:

Homework 2.8 says repeat the experiment 1000 times. Does it mean generate 1000 input training set +10% noise and find average E_in ? (as linear regression uses closed form solution for W, so there is nothing random if we run 1000 times on the same training set ).

Homework 2.9 says transform the training data set to a 6d feature space. If we have 1000 training sets created in 2.8, which one is 2.9 referring to?

Thanks a lot,

Yijun
#2
07-19-2012, 03:06 PM
 samirbajaj Member Join Date: Jul 2012 Location: Silicon Valley Posts: 48
Re: question on 2.8 and 2.9

Quote:
 Originally Posted by yijun2011@yahoo.com Could someone please help to clarify: ...(as linear regression uses closed form solution for W, so there is nothing random if we run 1000 times on the same training set ).
Correct, I generated 1000 new random points each time.

2.9 asks you about the weights.

-Samir
#3
07-19-2012, 03:07 PM
 yaser Caltech Join Date: Aug 2009 Location: Pasadena, California, USA Posts: 1,478
Re: question on 2.8 and 2.9

Quote:
 Originally Posted by yijun2011@yahoo.com Could someone please help to clarify
In this, and in all problems where we repeat an experiment for a number of runs and average the results of these runs, all specifications pertain to an individual run. For example, generating the training set, noise, and target function, is for an individual run. You then get results from that run and repeat the entire process a number of times (1000 in this case).

The goal is to average out statistical fluctuations that occur from run to run, so that the final result is indicative of the general behavior, rather than the particulars of any one training set or one target function, etc.
__________________
Where everyone thinks alike, no one thinks very much

 Thread Tools Display Modes Linear Mode

 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 Rules
 Forum Jump User Control Panel Private Messages Subscriptions Who's Online Search Forums Forums Home General     General Discussion of Machine Learning     Free Additional Material         Dynamic e-Chapters         Dynamic e-Appendices Course Discussions     Online LFD course         General comments on the course         Homework 1         Homework 2         Homework 3         Homework 4         Homework 5         Homework 6         Homework 7         Homework 8         The Final         Create New Homework Problems Book Feedback - Learning From Data     General comments on the book     Chapter 1 - The Learning Problem     Chapter 2 - Training versus Testing     Chapter 3 - The Linear Model     Chapter 4 - Overfitting     Chapter 5 - Three Learning Principles     e-Chapter 6 - Similarity Based Methods     e-Chapter 7 - Neural Networks     e-Chapter 8 - Support Vector Machines     e-Chapter 9 - Learning Aides     Appendix and Notation     e-Appendices

All times are GMT -7. The time now is 09:00 AM.

 Contact Us - LFD Book - Top

Powered by vBulletin® Version 3.8.3
Copyright ©2000 - 2021, 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.