Learning Polynomial Networks for Classification of Clinical Electroencephalograms

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dc.creatorSchetinin, Vitaly
dc.creatorSchult, Joachim
dc.date2005-04-11
dc.date.accessioned2026-07-07T03:22:49Z
dc.date.available2026-07-07T03:22:49Z
dc.descriptionWe 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.
dc.identifierhttps://arxiv.org/abs/cs/0504041
dc.identifierhttp://arxiv.org/abs/cs/0504041
dc.identifierJ Soft Computing 2005
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/32705
dc.subjectArtificial Intelligence
dc.subjectNeural and Evolutionary Computing
dc.titleLearning Polynomial Networks for Classification of Clinical Electroencephalograms
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