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 -
- Learning corresponds to storing each observed training example in memory.
- Subsequent instances are classified by looking them up in the memory.
- If the instance is found in memory, the stored classification is returned.
- Otherwise, the system refuses to classify the new instance.
- Inductive Bias: There is no inductive bias.
- New instances are predicted/classified only in the case where all members of the current version space agree on the classification.
- Otherwise, the system refuses to classify the new, instance.
- Inductive Bias: The target concept can be represented in its hypothesis space.
- This algorithm finds the most specific hypothesis consistent with training examples.
- It then uses this hypothesis to classify all subsequent instances.
- 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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