Authors:
Ali Mahmoudi
1
;
Reza Askari Moghadam
1
and
Kurosh Madani
2
Affiliations:
1
Faculty of New Sciences and Technologies, University of Tehran, Tehran and Iran
;
2
LISSI Lab, Sénart-FB Institute of Technology, University Paris Est-Créteil (UPEC), Lieusaint and France
Keyword(s):
Online Classification, Probabilistic Neural Network, Sequential Learning, Machine Learning.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Health Engineering and Technology Applications
;
Higher Level Artificial Neural Network Based Intelligent Systems
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
In this paper, a novel online classification algorithm called sequential heteroscedastic probabilistic neural network (SHPNN) is proposed. This algorithm is based on Probabilistic Neural Networks (PNNs). One of the advantages of the proposed algorithm is that it can increase the number of its hidden node kernels adaptively to match the complexity of the data. The performance of this network is analyzed for a number of standard datasets. The results suggest that the accuracy of this algorithm is on par with other state of the art online classification algorithms while being significantly faster in majority of cases.