(New page: Category:ECE662Spring2012Boutin Category:blog =Lecture 11 Blog, ECE662 Spring 2012, Prof. Boutin= Tuesday February 14, 2012 (Week 9) ---- Quick link to lectu...) |
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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]]|[[ | + | 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 studied a different way to formulate Bayes error in the two category case. The key idea in this new formulation is to view the discriminant function as a random variable. By changing the integration variable from the feature vector to the discriminant function, we end up having to compute two 1D integrations, as opposed to two an n-dimensional integrations, each on a complex domain. We illustrate how this new formulation can be used to explicitely obtain an analytical expression for Bayes error in the case where the feature vectors are normally distributed with the same standard deviation matrix for both classes. | Today we studied a different way to formulate Bayes error in the two category case. The key idea in this new formulation is to view the discriminant function as a random variable. By changing the integration variable from the feature vector to the discriminant function, we end up having to compute two 1D integrations, as opposed to two an n-dimensional integrations, each on a complex domain. We illustrate how this new formulation can be used to explicitely obtain an analytical expression for Bayes error in the case where the feature vectors are normally distributed with the same standard deviation matrix for both classes. | ||
Revision as of 08:22, 14 February 2012
Lecture 11 Blog, ECE662 Spring 2012, Prof. Boutin
Tuesday February 14, 2012 (Week 9)
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 studied a different way to formulate Bayes error in the two category case. The key idea in this new formulation is to view the discriminant function as a random variable. By changing the integration variable from the feature vector to the discriminant function, we end up having to compute two 1D integrations, as opposed to two an n-dimensional integrations, each on a complex domain. We illustrate how this new formulation can be used to explicitely obtain an analytical expression for Bayes error in the case where the feature vectors are normally distributed with the same standard deviation matrix for both classes.
Action items
- Don't forget to hand in your report for the first homework tonight!
Previous: Lecture 10
Next: Lecture 12
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