
#1




Q10 Clarification required
I am not exactly sure of the intent of the question. So would like some clarification.
How do you define perceptron learning algorithm? Is it an algorithm that recalculates weights to classify training example one point at a time ? I am asking this because to me perceptron learning algothm has two properties: 1. Recalculate weights to classify training example one point 2. adjust weights if a point is misclassified by adding w*x to the weight Now if I change the mechanism of weight adjustment to SGD, does the algorithm still remain perceptron ? In short, what is a formal definition of perceptron algorithm ? 
#2




Re: Q10 Clarification required
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You are looking for an error function that will essentially make the SGD behave like a perceptron in updating the weights. 
#3




Re: Q10 Clarification required
Perceptron algorithm updates the weight only if the point is misclassified. The same should be true using SGD, correct ?

#4




Re: Q10 Clarification required
I think that the question wants us to choose which error measure, when implemented using the SGD method, would produce exactly the same change to weights as the perceptron learning algorithm taught back at the start of the course.
So if there is no error for the selected data point, then there is no change to the weights. If there is an error for the selected data point, then the weights are increased by yn * xn Is that the understanding of others? 
#5




Re: Q10 Clarification required
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#6




Re: Q10 Clarification required
Ah  that makes sense. I had just looked for any old error function that will solve the Perceptron classification problem (one of them seemed like it would obviously work), and got the wrong answer.

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