LFD Book Forum Bias-Variance Analysis
 Register FAQ Calendar Mark Forums Read

#11
06-21-2015, 06:21 AM
 prithagupta.nsit Junior Member Join Date: Jun 2015 Posts: 7
Re: Bias-Variance Analysis

Thank you professor, I think I can work from here.
#12
03-05-2017, 12:44 PM
 Jackwsimpson Junior Member Join Date: Mar 2017 Location: On a boat ranging over the eastern third of US & Canada 9 mo, and Sarasota FL the other 3. Posts: 1
Re: Bias-Variance Analysis

I am confused in trying to get from the first line to the second line for the first set of equations on page 63: ... ED[Ex[(g... on the first line to ...Ex[ED[( on the second line.

I sort of see the first line: expected value with respect to data set x (a subset of D I assume) is averaged over all possible data set x's in D. On the second line we have what might be the average of the argument over all of D inside the outer brackets. I don't know how to interpret Ex outside the outer brackets.

In short, I certainly don't understand what exactly is meant by the 2nd line, and I may well not understand the first line. Any further explanation possible?

 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 08:42 AM.