1 citations · 1 across the 5 of their papers we have counts for
5 papers
The Combined Technique for Detection of Artifacts in Clinical Electroencephalograms of Sleeping Newborns
Vitaly Schetinin, Joachim Schult
In this paper we describe a new method combining the polynomial neural network and decision tree techniques in order to derive comprehensible classification rules from clinical ele…
A Neural-Network Technique to Learn Concepts from Electroencephalograms
Vitaly Schetinin, Joachim Schult
A new technique is presented developed to learn multi-class concepts from clinical electroencephalograms. A desired concept is represented as a neuronal computational model consist…
Neural-Network Techniques for Visual Mining Clinical Electroencephalograms
Vitaly Schetinin, Joachim Schult, Anatoly Brazhnikov
In this chapter we describe new neural-network techniques developed for visual mining clinical electroencephalograms (EEGs), the weak electrical potentials invoked by brain activit…
Learning Multi-Class Neural-Network Models from Electroencephalograms
Vitaly Schetinin, Joachim Schult, Burkhart Scheidt +1
We describe a new algorithm for learning multi-class neural-network models from large-scale clinical electroencephalograms (EEGs). This algorithm trains hidden neurons separately t…
Learning Polynomial Networks for Classification of Clinical Electroencephalograms
Vitaly Schetinin, Joachim Schult
We describe a polynomial network technique developed for learning to classify clinical electroencephalograms (EEGs) presented by noisy features. Using an evolutionary strategy impl…