#1




LRA > PLA Effect of Alpha
I noticed that when running the Linear Regression on a training data set followed by running the PLA using the same data and LRA weights, that the Learning Rate (Alpha) of the PLA seems to significantly effect the rate of convergence. I am assuming that the optimal size of alpha is directly related to the size of the convergence errors from the Linear Regression.
Is there a way to model this mathematically such that the Alpha parameter can automatically be calculated for each training set? 
#2




Re: LRA > PLA Effect of Alpha
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#3




Re: LRA > PLA Effect of Alpha
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The size of alpha doesn't always seem to matter but there are specific cases of where the appropriately assigned is able to drop the number of iterations down by an additional 50%75%. I am going to chew on this for a little while and see if I can figure out the relationship. 
#4




Re: LRA > PLA Effect of Alpha
No one ever said the PLA was a *good* algorithm. It's only guaranteed to converge eventually. I'm sure later in the lecture we'll get to better optimization algorithms.

#5




Re: LRA > PLA Effect of Alpha
Quote:
As the problem is done with then, as you note, the effect is small. What it seems is that if the LRA solution correctly classifies the points, then no cycles of PLA are used, otherwise just about as many as before. The 50% is the cases where no cycles of PLA are used. 
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