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If P(  m | x)  1 , then the nearest neighbor selection is almost always the same as the Bayes selection

Pattern Classification All materials in these slides were taken from Pattern Classification (2nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John Wiley & Sons, 2000 with the permission of the authors and the publisher.

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If P(  m | x)  1 , then the nearest neighbor selection is almost always the same as the Bayes selection

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  1. Pattern ClassificationAll materials in these slides were taken from Pattern Classification (2nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John Wiley & Sons, 2000with the permission of the authors and the publisher

  2. If P(m | x)  1, then the nearest neighbor selection is almost always the same as the Bayes selection

  3. The k – nearest-neighbor rule • Goal: Classify x by assigning it the label most frequently represented among the k nearest samples and use a voting scheme

  4. Example: k = 3 (odd value) and x = (0.10, 0.25)t Closest vectors to x with their labels are: {(0.10, 0.28, 2); (0.12, 0.20, 2); (0.15, 0.35,1)} One voting scheme assigns the label 2 to x since 2 is the most frequently represented

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