Introduction to Information Retrieval

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Introduction to Information RetrievalSec.14.3Nearest-Neighbor Learning Algorithm• Learning is just storing the representations of the training examplesin 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 categoryprototypes.• Also called:• Case-based learning• Memory-based learning• Lazy learning4
Introduction to Information Retrievalk Nearest Neighbor (kNN)Sec.14.3Classification• For k = 1 (1NN), we assign each test document to the class of itsnearest neighbor in the training set.• 1NN is not very robust – one document can be mislabeled oratypical.• For k > 1, we assign each test document to the majority class ofits k nearest neighbors in the training set.• This amounts to locally defined decision boundaries betweenclasses – far away points do not influence the classificationdecision. (different from Rocchio)• Rationale of kNN: contiguity hypothesis• We expect a test document d to have the same label as thetraining documents located in the local region surrounding d.
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Introduction to Information Retrieval

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