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2. adjust weights if a point is misclassified by adding w*x to the weight.
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-> i think you might have typo-ed, w(t+1) = w(t) + y(t)*x(t) for perceptron.
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1. Recalculate weights to classify training example one point
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With the updated weights, recalculate y_n for each x_n in the training set, and determine which ones are misclassified.
You are looking for an error function that will essentially make the SGD behave like a perceptron in updating the weights.