  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.
