Combining Multiple Classifiers in Probabilistic Neural Networks

Authors:
Jiri Grim;Josef Kittler;Pavel Pudil;Petr Somol
Affiliations:
-;-;-;-
Venue:
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Year:
2000

Citing 8
Cited 2

Combining the results of several neural network classifiers

Neural Networks
On Combining Classifiers

IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Neural Network Ensembles

IEEE Transactions on Pattern Analysis and Machine Intelligence
On Virtually Binary Nature of Probabilistic Neural Networks

SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Example Based Learning for View-Based Human Face Detection

Example Based Learning for View-Based Human Face Detection
Multiple network fusion using fuzzy logic

IEEE Transactions on Neural Networks
Improving model accuracy using optimal linear combinations of trained neural networks

IEEE Transactions on Neural Networks

Information Analysis of Multiple Classifier Fusion

MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Improving protein-ATP binding residues prediction by boosting SVMs with random under-sampling

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

We first summarize main features of a new probabilistic approach to neural networks recently developed in a series of papers in the framework of statistical pattern recognition. We consider a simplifying binary approximation of the output variables and, in order to prevent the arising information loss, we propose to combine multiple solutions. However, instead of combining different a posteriori probabilities, we make a parallel use of the binary output vectors to compute the standard Bayesian classifier.