Paper Info
Title
Simple and effective connectionist nonparametric estimation of probability density functions
Abstract
Estimation of probability density functions (pdf) is one major topic in pattern recognition. Parametric techniques rely on an arbitrary assumption on the form of the underlying, unknown distribution. Nonparametric techniques remove this assumption In particular, the Parzen Window (PW) relies on a combination of local window functions centered in the patterns of a training sample. Although effective, PW suffers from several limitations. Artificial neural networks (ANN) are, in principle, an alternative family of nonparametric models. ANNs are intensively used to estimate probabilities (e.g., class-posterior probabilities), but they have not been exploited so far to estimate pdfs. This paper introduces a simple neural-based algorithm for unsupervised, nonparametric estimation of pdfs, relying on PW. The approach overcomes the limitations of PW, possibly leading to improved pdf models. An experimental demonstration of the behavior of the algorithm w.r.t. PW is presented, using random samples drawn from a standard exponential pdf.
Year
DOI
Venue
2006
10.1007/11829898_1
ANNPR
Keywords
Field
DocType
standard exponential pdf,simple neural-based algorithm,nonparametric model,alternative family,parzen window,effective connectionist nonparametric estimation,improved pdf model,nonparametric technique,nonparametric estimation,algorithm w,probability density function,arbitrary assumption,pattern recognition,random sampling
Pattern recognition,Computer science,Posterior probability,Nonparametric statistics,Parametric statistics,Exponential distribution,Artificial intelligence,Artificial neural network,Probability density function,Machine learning,Kernel density estimation,Window function
Conference
Volume
ISSN
ISBN
4087
0302-9743
3-540-37951-7
Citations 
PageRank 
References 
4
0.49
3
Authors
1
Name
Order
Citations
PageRank
Edmondo Trentin128629.25