LFD Book Forum Question 2 - Meaning of /g

#1
09-07-2012, 12:48 PM
 TonySuarez Member Join Date: Jul 2012 Location: Lisboa, Portugal Posts: 35
Question 2 - Meaning of /g

I would like to clarify the meaning of in question 2 of the final exam, if possible...

Suppose the polynomial model which is applied to all X in a data (sub)set D. There are data subsets, indexed by d. Under this model, is considered to be:

or

where

?

In other words, we average the "functional form" of the function, or we average the parameters of the function over the training subsets?

#2
09-07-2012, 01:19 PM
 yaser Caltech Join Date: Aug 2009 Location: Pasadena, California, USA Posts: 1,477
Re: Question 2 - Meaning of /g

Quote:
 Originally Posted by TonySuarez In other words, we average the "functional form" of the function, or we average the parameters of the function over the training subsets?
The average of the functional form.
__________________
Where everyone thinks alike, no one thinks very much
#3
09-07-2012, 01:33 PM
 TonySuarez Member Join Date: Jul 2012 Location: Lisboa, Portugal Posts: 35
Re: Question 2 - Meaning of /g

Quote:
 Originally Posted by yaser The average of the functional form.
Thank you very much Professor.

TS

 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 03:23 PM.