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


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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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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

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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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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
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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