LFD Book Forum Problem 2.16

#1
03-12-2015, 11:26 AM
 NewtoML Junior Member Join Date: Mar 2015 Posts: 8
Problem 2.16

Hi,

1. I'm trying to solve Problem 2.16, but somewhat confused by the notation. Does the "c" subscript in "hc" refer to the hypothesis number? If so, can I assume that c is a positive integer c= 0,1,2,3 etc? If not, what is c, and c subscript i? (I couldn't find any subscripts used in this way in the chapters of the book.)

2. I can solve 2.16 by showing that this is equivalent to a perceptron in d dimensions, with d=1, and then using the techniques used in your (excellent) video lecture to show that the VC dimension is exactly d+1. However, is there a simpler way to answer the question?

Thank you!
#2
03-14-2015, 09:18 AM
 htlin NTU Join Date: Aug 2009 Location: Taipei, Taiwan Posts: 601
Re: Problem 2.16

is the vector whose components are so each vector serves as an index to a hypothesis.

Mimicking the proof for the VC dimension of perceptrons sound like a plausible way of conquering the problem. Hope this helps.
__________________
When one teaches, two learn.
#3
03-14-2015, 12:31 PM
 NewtoML Junior Member Join Date: Mar 2015 Posts: 8
Re: Problem 2.16

It does. Thank you very much for responding!

 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 10:23 PM.