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K_Nearest_Neighbor_Algorithm.pdf

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WEIGHTED K NEAREST NEIGHBOR Siddharth Deokar CS 8751 04/20/2009 deoka001@d.umn.edu Outline Background Simple KNN KNN by Backward Elimination Gradient Descent & Cross Validation Gradient Descent & Cross Validation Instance Weighted KNN Attribute Weighted KNN Results Implementation DIET Background K Nearest Neighbor Lazy Learning Algorithm Defer the decision to generalize beyond the training examples till a new query is encountered Whenever we have a new point to classify, we find it
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  WEIGHTED K NEAREST NEIGHBOR Siddharth DeokarCS 875104/20/2009deoka001@d.umn.edu  Outline  Background  Simple KNN  KNN by Backward Elimination  Gradient Descent & Cross Validation   Instance Weighted KNN  Attribute Weighted KNN  Results  Implementation  DIET  Background  K Nearest Neighbor  Lazy Learning AlgorithmDeferthedecisiontogeneralizebeyondthetrainingexamplestillanewqueryisencountered  eneverweaveanewponttocassy,wentsnearestneighborsfromthetrainingdata.  Thedistanceiscalculatedusingoneofthefollowingmeasures  Euclidean Distance  MinkowskiDistance  MahalanobisDistance  Simple KNN Algorithm  Foreachtrainingexample<x,f(x)>,addtheexampletothelistoftraining_examples. q ,  Letx 1 ,x 2 ….x k denotethekinstancesfromtraining_examplesthatarenearesttox q .  Returntheclassthatrepresentsthemaximumofthekinstances.

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Jul 23, 2017
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