m
 
(One intermediate revision by one other user not shown)
Line 1: Line 1:
The non-parametric density estimation is
+
[[Category:ECE662]]
 
+
P(x) = k/(NV)
+
 
+
where, k is the number of samples in V, N is the total number of samples, and V is the volume surrounding x.
+
 
+
This estimate is computed by two approaches
+
 
+
1) Parzen window approach
+
  - Fixing the volume V and determining the number k of data points inside V
+
 
+
2) KNN(K-Nearest Neighbor)
+
- Fixing the value of k and determining the minimum volume V that encompasses k points in the dataset
+
 
+
 
+
* The advantages of non-parametric techniques
+
- No assumption about the distribution required ahead of time
+
- With enough samples we can converge to an target density
+
 
+
* The disadvantages of non-parametric techniques
+
- If we have a good classification, the number of required samples may be very large
+
- Computationally expensive
+

Latest revision as of 08:45, 10 April 2008

Alumni Liaison

To all math majors: "Mathematics is a wonderfully rich subject."

Dr. Paul Garrett