Learning Polynomial Networks for Classification of Clinical Electroencephalograms

2005/04/11 by Vitaly Schetinin, Joachim Schult
Computer Science · #cs.AI #cs.NE

paper · pdf

published as J Soft Computing 2005

arxiv created 2005/04/11 · arxiv updated 2009/12/01

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.

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