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=Lecture 20 Blog, [[ECE662]] Spring 2012, [[user:mboutin|Prof. Boutin]]= | =Lecture 20 Blog, [[ECE662]] Spring 2012, [[user:mboutin|Prof. Boutin]]= | ||
| − | + | Thursday March 22, 2012 (Week 10) | |
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Quick link to lecture blogs: [[Lecture1ECE662S12|1]]|[[Lecture2ECE662S12|2]]|[[Lecture3ECE662S12|3]]|[[Lecture4ECE662S12|4]]|[[Lecture5ECE662S12|5]]|[[Lecture6ECE662S12|6]]|[[Lecture7ECE662S12|7]]|[[Lecture8ECE662S12|8]]| [[Lecture9ECE662S12|9]]|[[Lecture10ECE662S12|10]]|[[Lecture11ECE662S12|11]]|[[Lecture12ECE662S12|12]]|[[Lecture13ECE662S12|13]]|[[Lecture14ECE662S12|14]]|[[Lecture15ECE662S12|15]]|[[Lecture16ECE662S12|16]]|[[Lecture17ECE662S12|17]]|[[Lecture18ECE662S12|18]]|[[Lecture19ECE662S12|19]]|[[Lecture20ECE662S12|20]]|[[Lecture21ECE662S12|21]]|[[Lecture22ECE662S12|22]]|[[Lecture23ECE662S12|23]]|[[Lecture24ECE662S12|24]]|[[Lecture25ECE662S12|25]]|[[Lecture26ECE662S12|26]]|[[Lecture27ECE662S12|27]]|[[Lecture28ECE662S12|28]]|[[Lecture29ECE662S12|29]]|[[Lecture30ECE662S12|30]] | Quick link to lecture blogs: [[Lecture1ECE662S12|1]]|[[Lecture2ECE662S12|2]]|[[Lecture3ECE662S12|3]]|[[Lecture4ECE662S12|4]]|[[Lecture5ECE662S12|5]]|[[Lecture6ECE662S12|6]]|[[Lecture7ECE662S12|7]]|[[Lecture8ECE662S12|8]]| [[Lecture9ECE662S12|9]]|[[Lecture10ECE662S12|10]]|[[Lecture11ECE662S12|11]]|[[Lecture12ECE662S12|12]]|[[Lecture13ECE662S12|13]]|[[Lecture14ECE662S12|14]]|[[Lecture15ECE662S12|15]]|[[Lecture16ECE662S12|16]]|[[Lecture17ECE662S12|17]]|[[Lecture18ECE662S12|18]]|[[Lecture19ECE662S12|19]]|[[Lecture20ECE662S12|20]]|[[Lecture21ECE662S12|21]]|[[Lecture22ECE662S12|22]]|[[Lecture23ECE662S12|23]]|[[Lecture24ECE662S12|24]]|[[Lecture25ECE662S12|25]]|[[Lecture26ECE662S12|26]]|[[Lecture27ECE662S12|27]]|[[Lecture28ECE662S12|28]]|[[Lecture29ECE662S12|29]]|[[Lecture30ECE662S12|30]] | ||
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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== | ||
| − | *[[ | + | *[[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]] | ||
| + | *[[HW3_KNNandNN_comp_zge|discussion: is KNN the same as NN when k=1?]] | ||
Latest revision as of 02:40, 12 April 2012
Lecture 20 Blog, ECE662 Spring 2012, Prof. Boutin
Thursday March 22, 2012 (Week 10)
Quick link to lecture blogs: 1|2|3|4|5|6|7|8| 9|10|11|12|13|14|15|16|17|18|19|20|21|22|23|24|25|26|27|28|29|30
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
- Lecture 17 from ECE662 Spring 2008
- Lecture 18 from ECE662 Spring 2008
- Lecture 19 from ECE662 Spring 2008
- discussion: is KNN the same as NN when k=1?
Previous: Lecture 19
Next: Lecture 21
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