Towards Simple and Effective Connectionist Nonparametric Estimation of Probability Density Functions
par/by Edmondo Trentin
Dipartimento di Ingegneria dell'Informazione
Universita' di Siena, Roma
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. The talk 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. A simple experimental
demonstration of the behavior of the algorithm w.r.t. PW is presented.