LFD Book Forum Proof of Growth Function when Points are not binary
 User Name Remember Me? Password
 Register FAQ Calendar Mark Forums Read

 Thread Tools Display Modes
#1
03-27-2013, 03:12 PM
 udaykamath Junior Member Join Date: Jan 2013 Posts: 9
Proof of Growth Function when Points are not binary

Prof Dr Yaser
I understand the step by step process in bounding the growth function B(N,k) in terms of recursion using x1,x2...xN data points using structural arrangements. I however started thinking, in a numerical data where x1,x2...xN is real numbers we cannot use this alpha + 2* Beta trick and use the polynomial bound by combination? What is the bounds when data is not binary ? Is it still Polynomial in N, and how?
Thanks

Uday Kamath
PhD candidate
GMU
#2
04-08-2013, 01:43 PM
 yaser Caltech Join Date: Aug 2009 Location: Pasadena, California, USA Posts: 1,478
Re: Proof of Growth Function when Points are not binary

Quote:
 Originally Posted by udaykamath I understand the step by step process in bounding the growth function B(N,k) in terms of recursion using x1,x2...xN data points using structural arrangements. I however started thinking, in a numerical data where x1,x2...xN is real numbers we cannot use this alpha + 2* Beta trick and use the polynomial bound by combination? What is the bounds when data is not binary ? Is it still Polynomial in N, and how?
You are correct. For non-binary functions, the argument is more elaborate. This is addressed in Vapnik's book "Statistical Learning Theory" in much detail, and also appears in other books and papers.
__________________
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 05:58 AM.

 Contact Us - LFD Book - Top