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Unlike parametric density estimation methods, non-parametric approaches locally estimate density function by a small number of neighboring samples [4] and therefore show less accurate estimation results. In spite of their accuracy, however, the performance of classifiers designed using these estimates is very satisfactory.\\
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Unlike parametric density estimation methods, non-parametric approaches locally estimate density function by a small number of neighboring samples [4] and therefore show less accurate estimation results. In spite of their accuracy, however, the performance of classifiers designed using these estimates is very satisfactory.
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The basic idea for estimating unknown density function is based on the fact that the probability $P$ that a vector \textbf{x} belongs to a region $R$ [1]:
  
 
Post your slecture material here. Guidelines:
 
Post your slecture material here. Guidelines:

Revision as of 07:54, 30 April 2014


Parzen window method and classification

A slecture by ECE student Chiho Choi

Partly based on the ECE662 Spring 2014 lecture material of Prof. Mireille Boutin.


in progess....


Unlike parametric density estimation methods, non-parametric approaches locally estimate density function by a small number of neighboring samples [4] and therefore show less accurate estimation results. In spite of their accuracy, however, the performance of classifiers designed using these estimates is very satisfactory.

The basic idea for estimating unknown density function is based on the fact that the probability $P$ that a vector \textbf{x} belongs to a region $R$ [1]:

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Basic linear algebra uncovers and clarifies very important geometry and algebra.

Dr. Paul Garrett