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The k-Nearest Neighbor Algorithm

  • All instances correspond to points in the n-D space
  • The nearest neighbor are defined in terms of Euclidean distance, dist(X1, X2)
  • Target function could be discrete- or real- valued
  • For discrete-valued, k-NN returns the most common value among the k training examples nearest to xq
  • Vonoroi diagram: the decision surface induced by 1-NN for a typical set of training examples


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