If we again use the linear perceptron algorithm to train the classifier, what will happen?
Note: In the choices below ,“converge" means given a certain input, the algorithm will terminate with a fixed output within finite steps (assume is very large: the output of the algorithm will not change as we increase ). Otherwise we say the algorithm diverges (even for an extremely large , the output of the algorithm will change as we increase further).
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It is not guaranteed that the linear perceptron algorithm will converge when training the classifier again. It depends on the data and the initial weights chosen. If the data is linearly separable and the initial weights are chosen appropriately, then the algorithm will converge and successfully train the classifier. However, if the data is not linearly separable or the initial weights are not chosen well, then the algorithm may diverge and not be able to find a solution.
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