LFD Book Forum Some questions about the lecture two
 Register FAQ Calendar Mark Forums Read

#1
02-08-2014, 06:19 PM
 LazyPiggy Junior Member Join Date: Feb 2014 Posts: 8
Some questions about the lecture two

Recently, I'm learning the video tutorials of learning from data . Two questions are lingering in my mind about the lecture two, which focused on the feasibility of learning: the first one is that how to define the feasibility of learning? Is the Hoeffding's Inequality the useful tool to gauge feasibility for one hypothesis h? take the hypothesis h1 and h2 for example, if both satisfy the Hoeffding's Inequality, then what we should do next? Another confusion is that in the case of mutiple h's, the simple solution to the modification of Hoeffding's Inequality could be useless as M is close to infinity. In my opinion ,the Hoeffding's Inequality seems to hold in this situation for the following reason:

P[ |Ein(g) − Eout(g)| > ǫ ] ≤ P[ |Ein(h1) − Eout(h1)| > ǫ
or |Ein(h2) − Eout(h2)| > ǫ· · ·
is no more than the minimum of them, that makes the Hoeffding's Inequality satisified. Is there a logical or mathematical error ? When it comes to the exception, I think that even the best learning algorithm could meet the special situation that only few of them could be equal to the target function for randomness, or we could consider this standard : for each hypothesis hi, i=1,2,...M, toss the coin N times, then calculate the possibility of the times that coins get all heads is less than one particular value t. such as N/4 or others. Is this standard viable? All responses are appreciated. Thank you.

 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 05:49 PM.