LFD Book Forum  

Go Back   LFD Book Forum > Course Discussions > Online LFD course > Homework 4

Reply
 
Thread Tools Display Modes
  #1  
Old 04-27-2012, 10:57 AM
lucag lucag is offline
Member
 
Join Date: Apr 2012
Posts: 44
Default Homework 4, exercises 2 and 3

Hello everybody,

I have a question regarding homework 4; in exercise 2 and 3, it is asked what is the best bound for large N and small N respectively.
Could anyone elaborate on what best means in this context?

Thanks in advance!
Luca
Reply With Quote
  #2  
Old 04-27-2012, 04:51 PM
yaser's Avatar
yaser yaser is offline
Caltech
 
Join Date: Aug 2009
Location: Pasadena, California, USA
Posts: 1,477
Default Re: Homework 4, exercises 2 and 3

Quote:
Originally Posted by lucag View Post
Hello everybody,

I have a question regarding homework 4; in exercise 2 and 3, it is asked what is the best bound for large N and small N respectively.
Could anyone elaborate on what best means in this context?

Thanks in advance!
Luca
Since these are upper bounds (that are valid), the smallest of them would be the best since it gives us the most specific information about the range of values our quantity (\epsilon in this case) is allowed to have.
__________________
Where everyone thinks alike, no one thinks very much
Reply With Quote
  #3  
Old 04-27-2012, 08:50 PM
lucag lucag is offline
Member
 
Join Date: Apr 2012
Posts: 44
Default Re: Homework 4, exercises 2 and 3

Thanks for the quick response!
I got it.

-Luca
Reply With Quote
Reply

Tags
generalization bound, generalization error, hw4, vc bound

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 04:32 PM.


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.