Re: HW 4 question3
thanks elkka , i don't know that I was thinking :(

Re: HW 4 question3
Quote:
To see that your example probably doesn't make sense (IMHO): replace the minutes in your example with either nanoseconds or, on the other hand, ages, and you would get very different numbers on the left side of the equation (i.e. epsilon) while it wouldn't make a difference for the right side of the equation. This can't be right (it would e.g. be unlikely that E_in and E_out are 60 seconds apart but likely that they are a minute apart?!): it would make the inequalities meaningless. Also on the slides of lecture 6, it is fractions (in)correctly classified that are used for the VapnikChervonenkis Inequality. Dislaimer: I'm not an expert on the matter, and perhaps I miss a/the point somewhere, so hope we'll get a verdict by the course staff. 
Re: HW 4 question3
You know, I think you are right. We are indeed only talking about classification problem, so E_in and E_out must be <= 1.

Re: HW 4 question3
Here is my view which can be wrong. Refer to lecture 4, slides 7 onwards.
Ein and Eout are the average of the error measure per point. And it is up to the user to choose the error measure. So Ein and Eout are just numbers and not probabilities. And so epsilon which is the difference between the two, is also a number. Also see lecture 8, slides 15 and 20: Eout = bias + variance = 0.21 + 1.69 > 1 
Re: HW 4 question3
Quote:

Re: HW 4 question3
Could someone from the course staff perhaps weigh in on this? There seem to be two equally valid theories....

Re: HW 4 question3
Quote:

Re: HW 4 question3
It suggested to use the simple approximate bound N^d_vc for the growth function, if N > d_vc. In Problem 3, N=5<d_vc=50. Should we still use N^d_vc as an approximation for the growth function? Or, maybe it is more reasonable to use 2^N, assuming that H is complex enough?

Re: HW 4 question3
Quote:

All times are GMT 7. The time now is 07:54 PM. 
Powered by vBulletin® Version 3.8.3
Copyright ©2000  2020, Jelsoft Enterprises Ltd.
The contents of this forum are to be used ONLY by readers of the Learning From Data book by Yaser S. AbuMostafa, Malik MagdonIsmail, and HsuanTien Lin, and participants in the Learning From Data MOOC by Yaser S. AbuMostafa. No part of these contents is to be communicated or made accessible to ANY other person or entity.