Learning polynomial networks for classification of clinical electroencephalograms

Authors:
V. Schetinin;J. Schult
Affiliations:
Department of Computer Science, University of Exeter, EX4 4QF, Exeter, UK;TheorieLabor, University of Jena, D 07740, Jena, Germany
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Year:
2006

Citing 0
Cited 4

Fault diagnosis and condition surveillance for plant rotating machinery using partially-linearized neural network

Computers and Industrial Engineering
Financial distress prediction in banks using Group Method of Data Handling neural network, counter propagation neural network and fuzzy ARTMAP

Knowledge-Based Systems
Intelligent diagnosis method for rolling element bearing faults using possibility theory and neural network

Computers and Industrial Engineering
Kernel group method of data handling: application to regression problems

SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing

Quantified Score

Hi-index	0.04

Visualization

Abstract

We describe a polynomial network technique developed for learning to classify clinical electroencephalograms (EEGs) presented by noisy features. Using an evolutionary strategy implemented within group method of data handling, we learn classification models which are comprehensively described by sets of short-term polynomials. The polynomial models were learnt to classify the EEGs recorded from Alzheimer and healthy patients and recognize the EEG artifacts. Comparing the performances of our technique and some machine learning methods we conclude that our technique can learn well-suited polynomial models which experts can find easy-to-understand.