PLA Optimization criteria
The PLA algorithm, eqn. 1.3, can be used to partition linearly separable data. What I'm curious is to what optimization criteria underlies eqn. 1.3? The figures on pp. 67 show that for a 2D case we have the algorithm converge to some straight line decision boundary, and it is also qualitatively clear that many different straightlines, would "work" equally well (ie give the same E_{in} error rate); however PLA converges to a specific solution. The PLA algorithm seems to provide both, an optimization criteria, and a method for solution too. The opt. criteria gives provides uniqueness. Can the optimization criteria underlying PLA (eqn 1.3) be spelled out explicitly? Thank you.

Re: PLA Optimization criteria
Quote:

Re: PLA Optimization criteria
A few weeks ago I recall having read that section on SGD, however the connection with PLA somehow slipped past. My sincere apologies. Then, I suppose I was trying keep my focus on ML paradigms & approaches, and much as optimization is part & parcel of ML, I think I tried not to get sidetracked with finer details of optimization. Lately, I've started rereading the book, a bit more carefully, and find myself appreciating the whole, and the subtle, even more so than before! :) Thank you for taking the trouble of pointing out the section. I do appreciate it.

All times are GMT 7. The time now is 05:42 AM. 
Powered by vBulletin® Version 3.8.3
Copyright ©2000  2021, 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.