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Today we talked about the nearest neighbor decision rule. We pointed out that this rule can obtained as a special case of a (biased estimate) formula for estimating the mixture density at a point x using the nearest neighbor among a set of labeled samples drawn from the mixture density.  
 
Today we talked about the nearest neighbor decision rule. We pointed out that this rule can obtained as a special case of a (biased estimate) formula for estimating the mixture density at a point x using the nearest neighbor among a set of labeled samples drawn from the mixture density.  
 
==Related Rhea Pages==
 
==Related Rhea Pages==
*[[KNN-K_Nearest_Neighbor_OldKiwi|A page about K-Nearest Neighbor from ECE662 Spring 2008]]
+
*[[Lecture_17_-_Nearest_Neighbors_Clarification_Rule_and_Metrics_OldKiwi|Lecture 17 from ECE662 Spring 2008]]
 +
*[[Lecture_18_-_Nearest_Neighbors_Clarification_Rule_and_Metrics(Continued)_OldKiwi|Lecture 18 from ECE662 Spring 2008]]
 +
*[[Lecture_19_-_Nearest_Neighbor_Error_Rates_OldKiwi|Lecture 19 from ECE662 Spring 2008]]
 
*[[KNN_algorithm_OldKiwi|A KNN tutorial, from ECE662 Spring 2008]]
 
*[[KNN_algorithm_OldKiwi|A KNN tutorial, from ECE662 Spring 2008]]
  

Revision as of 09:42, 22 March 2012


Lecture 20 Blog, ECE662 Spring 2012, Prof. Boutin

Wednesday March 22, 2012 (Week 10)


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Today we talked about the nearest neighbor decision rule. We pointed out that this rule can obtained as a special case of a (biased estimate) formula for estimating the mixture density at a point x using the nearest neighbor among a set of labeled samples drawn from the mixture density.

Related Rhea Pages


Previous: Lecture 19

Next: Lecture 21


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