JNTU B.Tech IV MACHINE LEARNING Previous Question Paper (September - 2020 - R16)

The CANDIDATE-ELIMINATION algorithm converges to the correct hypothesis if and only if two conditions are met: the target concept is contained within the hypothesis space considered by the algorithm, and the training data provided is noise-free. If the target concept is not in the hypothesis space, the algorithm will converge to an empty version space, indicating no consistent hypothesis exists. If the data contains noise, the algorithm may incorrectly eliminate the true target hypothesis, leading to an empty version space even when a solution exists. When these conditions are satisfied, the algorithm maintains the version space of all hypotheses consistent with the training examples, and as more data is provided, the version space shrinks until it contains only the target concept.

The Specific Boundary (S) is the set of the most specific hypotheses within the version space that are consistent with the observed training examples. It represents the minimal generalizations required to cover all positive examples seen so far, ensuring no negative examples are covered.

The General Boundary (G) is the set of the most general hypotheses within the version space that are consistent with the observed training examples. It represents the maximal generalizations that still exclude all negative examples while covering all positive examples.

Together, these boundaries define the Version Space, where any hypothesis h that is more general than or equal to some member of S and more specific than or equal to some member of G is consistent with the training data.