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S = Training data = => Yes (positive example). How will S be represented after encountering this training data?

S = Training data = => Yes (positive example). How will S be represented after encountering this training data? Correct Answer <square, pointy, white >

Initially, S contains phi, which implies that no example is positive. It encounters a positive example, which is inconsistent with the current hypothesis. So, it generalizes accordingly to approve the new example. It thus takes the values of the training instance.

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