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Module 3: Classification and Prediction

20202m

Case-based learning is

(i) a class of learning algorithm that tries to find an optimum classification of a set of examples using the probabilistic theory
(ii) any mechanism employed by a learning system to constrain the search space of a hypothesis
(iii) an approach to the design of learning algorithms that is inspired by the fact that when people encounter new situations, they often explain them by reference to familiar experiences, adapting the explanations to fit the new situation
(iv) None of the above

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Answer: (iii) an approach to the design of learning algorithms that is inspired by the fact that when people encounter new situations, they often explain them by reference to familiar experiences, adapting the explanations to fit the new situation

Case-based learning (case-based reasoning, CBR) solves a new problem by recalling and adapting solutions of similar stored past cases — mirroring how humans refer to familiar experiences and adapt them to a new situation. It is a lazy / instance-based learning approach.

Why the others are wrong:

  • (i) describes a probabilistic (Bayesian) classifier, not case-based learning.
  • (ii) describes an inductive-bias / hypothesis-space constraining mechanism, not case-based learning.
  • (iv) None of the above — incorrect since (iii) is correct.

Hence case-based learning is the "adapt familiar experiences to new situations" learning paradigm — option (iii).

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