$2\mathrm{.}1$

$1\mathrm{.}0$ point possible $($graded$,$ results hidden$)$

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 $T$ 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$).$

The algorithm always converges and we get a classifier that perfectly classifies the training dataset.

The algorithm always converges and we get a classifier that does not perfectly classifies the training

dataset.

The algorithm will never converge.

The algorithm might converge for some initial input of $,{}_0$ and certain sequence of the data, but will

diverge otherwise. When it converges, we always get a classifier that does not perfectly classifies the

training dataset.

The algorithm might converge for some initial input of $,{}_0$ and certain sequence of the data, but will

diverge otherwise. When it converges, we always get a classifier that perfectly classifies the training

dataset.