#1




Problem 1.5 Adaline
I had bad luck with the ALA: for all but the smallest training data sets and with more than 2 dimensions, the weights would go scooting off to infinity.
I modified the algorithm so as to become a regression vs categorization problem, I changed the update criteria to be: Code:
s = np.dot(x[i,:], w) if np.abs(y[i]  s) > 0.01: w = w + eta * (y[i]  s) * x[i,:] n_updates += 1 So rather than choosing a plane that separates the data, this chooses the plan that gets the correct distance (within the 0.01) between the plan and the data point for every training data point. 
#2




Re: Problem 1.5 Adaline
Though this is interesting, on further thought, it seems to be quite useless. The y associated with each training point is the distance between that point to the separating plane. So you would have to know the plane to begin with..

#3




Re: Problem 1.5 Adaline
__________________
When one teaches, two learn. 
#4




Re: Problem 1.5 Adaline
Actually, it isn't that useless if the data happens to be that then adaline is a quick way of converging to a plane that fits the data. Yes, thanks for that wikipedia reference.

#5




Re: Problem 1.5 Adaline
Well Said Don!
Wikipedia reference was helpful  Thanks Htlin! 
Tags 
ala, classify vs regression 
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