Perceptron Learning Algorithm
I'm trying to program the perceptron example but can't figure out how to assign the sign of the first step.
I drew a line between two points on the xy plane [1,1] in both directions. Then I randomly generated another 10 points, assigning them +1 if they fell above the line and 1 if they fell below the line. I stored those values in ideal function g. Then I gave each point a random first guess of +1/1 as my initial function h. If I start with all the weights as 0, then w*x = 0 for all points. At that rate, PLA will never converge. What is my conceptual error? 
Re: Perceptron Learning Algorithm
Hi
I have a question about how weighting is assigned and the meaning of the PLA . For point a1 which has assignment 1, does w(a1.y) + w(a1.x) = 1 ? ( '.' denotes subscript) And then, for point a2 which has assignment 1, would w(a1.y) + w(a1.x) + w(a1.x) + w(a2.x) = 1 , and so on? To adjust weighting of w for misclassified points, is w.x2 = w.x1 + x.2 * y.2 Thank you for the help! 
Re: Perceptron Learning Algorithm
Quote:
Either point, call it just for simplicity, is a vector that has components . Notice that bold denotes a full data point, while italic denotes a component in that data point. We add a constant 1 component to each data point and call the component to simplify the expression for the perceptron. If the weight vector of the perceptron is (where takes care of the threshold value of that perceptron), then the perceptron implements where returns if its argument is positive and returns if its argument is negative. Example: Say the first data point (two dimensional, so ). Add the constant component and you have . Therefore, the percepton's output on this point is . If this formula returns which is different from the target output , the PLA adjusts the values of the weights trying to make the perceptron output agree with the target output for this point . It uses the specific PLA update rule to achieve that. 
Re: Perceptron Learning Algorithm
Thank you for your reply!
So, y.n for point 2 has nothing to do with point 1, correct? Also, since w.0, w.1 and w.2 are different numbers, do you need multiple points to assess the values of the three w's? Further, is updated w like an array, dependent on which n is being chosen, or is it one uniform number that is applied to all x's? 
Re: Perceptron Learning Algorithm
Quote:
All three components of the weight vector are simultaneously updated by PLA at each iteration, and there is more than one data point to update depending on which points are misclassified at a given time as the algorithm runs. 
Re: Perceptron Learning Algorithm
Hello!
I have some misuderstandings with PLA. Should we update w accordings to only one misclassified point at an iteration or through all the misclassified points at the given iteration? thanks 
Re: Perceptron Learning Algorithm
Quote:

Re: Perceptron Learning Algorithm
Quote:
I think it would be interesting if we can all input our actual numbers and you later show a histogram of what people entered on their homework solutions. ;) I'm shocked by the speed with which PLA converges. I never would have guessed that until I actually coded it up. This is a very interesting and intellectually satisfying exercise! I'm having a hard time deciding how to answer the multiple choice Q 710. The answer depends upon if I use log or linear scaling. Aren't CS algorithm efficiencies usually classified in log scaling? Or am I overthinking this? If an algorithm always converges would the Pr(f(x) ne g(x)) = 0? 
Re: Perceptron Learning Algorithm
Quote:
For my code, I consider 1 iteration as a scan from beginning to end through the entire data set x and correcting any misclassified points. I have already submitted my homework and I believe I cannot resubmit. Thanks, KK. 
All times are GMT 7. The time now is 04:05 PM. 
Powered by vBulletin® Version 3.8.3
Copyright ©2000  2022, 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.