Learning Algorithm used in Inductive Bias

Less than 500 views Posted On Aug. 21, 2020

Prerequisite: Inductive Bias in Machine Learning

Learning algorithms used in Inductive Bias are -
  1. Rote-Learner:
    1. Learning corresponds to storing each observed training example in memory.
    2. Subsequent instances are classified by looking them up in the memory.
    3. If the instance is found in memory, the stored classification is returned.
    4. Otherwise, the system refuses to classify the new instance.
    5. Inductive Bias: There is no inductive bias.
  2. Candidate-Elimination:
    1. New instances are predicted/classified only in the case where all members of the current version space agree on the classification.
    2. Otherwise, the system refuses to classify the new, instance.
    3. Inductive Bias: The target concept can be represented in its hypothesis space.
  3. FIND-S:
    1. This algorithm finds the most specific hypothesis consistent with training examples.
    2. It then uses this hypothesis to classify all subsequent instances.
    3. Inductive Bias: The target concept can be represented in its hypothesis space, and all instances are negative instances unless the opposite is entailed by its other knowledge.
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