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Learning is just storing the representations of the training examples in D.
Testing instance x (under 1NN):
Compute similarity between x and all examples in D.
Assign x the category of the most similar example in D.
Does not explicitly compute a generalization or category prototypes.
Rationale of kNN: contiguity hypothesis
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