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| − | == | + | [[Category:ECE662Spring2012Boutin]] |
| + | [[Category:blog]] | ||
| + | [[Category:density estimation]] | ||
| + | [[Category:decision theory]] | ||
| + | [[Category:pattern recognition]] | ||
| + | [[Category:k nearest neighbors]] | ||
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| + | =Lecture 19 Blog, [[ECE662]] Spring 2012, [[user:mboutin|Prof. Boutin]]= | ||
| + | Tuesday March 20, 2012 (Week 10) | ||
| + | ---- | ||
| + | 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]] | ||
| + | ---- | ||
| + | Coming back from Spring break, today we covered the k-nearest neighbor (KNN) density estimation technique, along with the k-nearest neighbor (KNN) pattern recognition method. More specifically, we presented a formula for estimating a density function at a point based on some samples drawn from that density, and we showed that it is an unbiased estimate of the true value of the density at that point. We also showed how this formula is the basis for using the "majority vote among neighbors" rule for assigning a category to data. | ||
| + | |||
| + | ==Related Rhea Pages== | ||
*[[KNN-K_Nearest_Neighbor_OldKiwi|A page about K-Nearest Neighbor from ECE662 Spring 2008]] | *[[KNN-K_Nearest_Neighbor_OldKiwi|A page about K-Nearest Neighbor from ECE662 Spring 2008]] | ||
*[[KNN_algorithm_OldKiwi|A KNN tutorial, from ECE662 Spring 2008]] | *[[KNN_algorithm_OldKiwi|A KNN tutorial, from ECE662 Spring 2008]] | ||
| + | |||
| + | |||
| + | Previous: [[Lecture18ECE662S12|Lecture 18]] | ||
| + | |||
| + | Next: [[Lecture20ECE662S12|Lecture 20]] | ||
| + | ---- | ||
| + | ==Comments== | ||
| + | Please write your comments and questions below. | ||
| + | *Write a comment here | ||
| + | *Write another comment here. | ||
| + | ---- | ||
| + | [[2012_Spring_ECE_662_Boutin|Back to ECE662 Spring 2012]] | ||
Latest revision as of 09:34, 22 March 2012
Lecture 19 Blog, ECE662 Spring 2012, Prof. Boutin
Tuesday March 20, 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
Coming back from Spring break, today we covered the k-nearest neighbor (KNN) density estimation technique, along with the k-nearest neighbor (KNN) pattern recognition method. More specifically, we presented a formula for estimating a density function at a point based on some samples drawn from that density, and we showed that it is an unbiased estimate of the true value of the density at that point. We also showed how this formula is the basis for using the "majority vote among neighbors" rule for assigning a category to data.
Related Rhea Pages
Previous: Lecture 18
Next: Lecture 20
Comments
Please write your comments and questions below.
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