LFD Book Forum (http://book.caltech.edu/bookforum/index.php)
-   Homework 2 (http://book.caltech.edu/bookforum/forumdisplay.php?f=131)
-   -   PLA (question 7): inital weight is too weak hint (http://book.caltech.edu/bookforum/showthread.php?t=3887)

 dobrokot 01-20-2013 02:57 PM

PLA (question 7): inital weight is too weak hint

If I take output of linear regression, I usually have a little (1-5 of 100) points which are misclassified. But it does not help PLA, because first addition Xi to W destabilize "almost good" value of W. I can even invert sign of L.R. output, it doesn't affect number of iterations (despite number of misclassified items on first iteration changes from 1-5 to 95-99, I verified)

But if I use (L.R. output)*N, it does help for PLA, greatly reduces number of iterations.

Is it supposed, that usage of linear regression output as W should greatly change number of iterations? If yes, does it means I should to search for other bugs in my code? :)

Output of L.R. should be used as is, or should be multiplied to make it "stronger" ?

 sanbt 01-20-2013 03:12 PM

Re: PLA (question 7): inital weight is too weak hint

Quote:
 Originally Posted by dobrokot (Post 8883) If I take output of linear regression, I usually have a little (1-5 of 100) points which are misclassified. But it does not help PLA, because first addition Xi to W destabilize "almost good" value of W. I can even invert sign of L.R. output, it doesn't affect number of iterations (despite number of misclassified items on first iteration changes from 1-5 to 95-99, I verified) But if I use (L.R. output)*N, it does help for PLA, greatly reduces number of iterations. Is it supposed, that usage of linear regression output as W should greatly change number of iterations? If yes, does it means I should to search for other bugs in my code? :) Output of L.R. should be used as is, or should be multiplied to make it "stronger" ?
I think for this question you should set N=10 and just use output of linear regression. It should reduce the number if iterations of PLA. I tried it for N = 100 and agree with you that linear regression doesn't help improve iterations.

 Anne Paulson 01-20-2013 07:50 PM

Re: PLA (question 7): inital weight is too weak hint

This issue is discussed in another thread. Short answer: scaling the weights by N might make a difference, but for the homework question, we should follow the directions as given and not scale.

 Michael Reach 04-03-2013 08:25 PM

Re: PLA (question 7): inital weight is too weak hint

Anne!:) Welcome back! (I was in your Data Analysis course.) Are you like a TA here or something? I know you took this course already.

 Anne Paulson 04-10-2013 11:46 AM

Re: PLA (question 7): inital weight is too weak hint

Hi Michael!

My post was from the last iteration of the course (look at the date). I'm not taking it this time. I only happened to see your post because I was randomly surfing around. Good luck to all the DA students taking this course now.

 Michael Reach 04-10-2013 12:11 PM

Re: PLA (question 7): inital weight is too weak hint

Oh, my bad. I did see your exalted Senior Member status and I thought that meant T.A. or such.

 wangkexue 04-15-2013 03:02 AM

Re: PLA (question 7): inital weight is too weak hint

My experiment results show that when N = 10, the iteration number will be reduced from about ** to about ** with LR Initiation. And when N = 100, the iteration number will be reduced from about ** to **. Futher, when N = 50, it be ** to about **. Does that means LR Initiation can only reduce the iteration number about **?

Admin Edit: Numbers taken out. Please start an *ANSWER* thread if you want to discuss specific answers.

 Elroch 04-15-2013 07:41 AM

*ANSWER* PLA (question 7): inital weight is too weak hint

wangxue, please could you delete (or edit) your last post, for obvious reasons?

 khohi 03-23-2016 06:05 AM

Re: PLA (question 7): inital weight is too weak hint

Great job
طريقة عمل البسبوسة

 All times are GMT -7. The time now is 08:51 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. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin, and participants in the Learning From Data MOOC by Yaser S. Abu-Mostafa. No part of these contents is to be communicated or made accessible to ANY other person or entity.