Difference between revisions of "Nonparametric Estimators OldKiwi" - Rhea
User
Log in
Actions
View source
History
Recent Changes
Slectures
Squad
Practice
Formulas
Donations
Disclaimer
Home
Print
Revision as of 22:21, 7 April 2008
(
view source
)
Gong1
(
Talk
)
← Older edit
Revision as of 22:24, 7 April 2008
(
view source
)
Gong1
(
Talk
)
(Removing all content from page)
Newer edit →
Line 1:
Line 1:
−
The non-parametric density estimation is
−
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
Revision as of 22:24, 7 April 2008
Shortcuts
Help
Main Wiki Page
Random Page
Special Pages
Log in
Search
Alumni Liaison
Followed her dream after having raised her family.
Read more »
Ruth Enoch, PhD Mathematics
Currently Active Pages
x
+
x
Chat
x