If we again use the linear perceptron algorithm to train the classifier, what will happen?

Question

Note: In the choices below ,“converge" means given a certain input, the algorithm will terminate with a fixed output within finite steps (assume T is very large: the output of the algorithm will not change as we increase T ). Otherwise we say the algorithm diverges (even for an extremely large T , the output of the algorithm will change as we increase T further).

Answer 1

It depends on the linear separability of the data. If the data is linearly separable, the linear perceptron algorithm will converge to a solution. If the data is not linearly separable, the algorithm will not converge and will continue to update the weights infinitely.