Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
164 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 12, NO. 1, JANUARY 2001 Comparison of Two Different PNN Training Approaches for Satellite Cloud
 

Summary: 164 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 12, NO. 1, JANUARY 2001
Comparison of Two Different PNN Training Approaches for Satellite Cloud
Data Classification
Bin Tian and Mahmood R. Azimi-Sadjadi
Abstract--This paper presents a training algorithm for proba-
bilistic neural networks (PNNs) using the minimum classification
error (MCE) criterion. A comparison is made between the MCE
training scheme and the widely used maximum likelihood (ML)
learning on a cloud classification problem using satellite imagery
data.
Index Terms--Cloud classification, maximum likelihood, min-
imum classification error, probabilistic neural network.
I. INTRODUCTION
Probabilistic neural network (PNN) is a kind of supervised
neural network that are widely used in the area of pattern recog-
nition, nonlinear mapping, and estimation of probability of class
membership and likelihood ratios. The original PNN structure
[1], is a direct neural-network implementation of the Parzen
nonparametric probability density function (PDF) estimation
[2] and Bayes classification rule. Although its training scheme

  

Source: Azimi-Sadjadi, Mahmood R. - Department of Electrical and Computer Engineering, Colorado State University

 

Collections: Computer Technologies and Information Sciences