Difference between revisions of "Nonparametric Estimators Old Kiwi" - Rhea

Latest revision as of 07:45, 10 April 2008

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-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
-V is the volume surrounding x
-This estimate is computed by two approaches
-) Parzen window approach
- ; Fixing the volume V and determining the number k of data points inside V
-) 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