16 citations · 45 across the 17 of their papers we have counts for
17 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…
Self-Organization of the Neuron Collective of Optimal Complexity
V. Schetinin, A. Kostunin
The optimal complexity of neural networks is achieved when the self-organization principles is used to eliminate the contradictions existing in accordance with the K. Godel theorem…
An Evolving Cascade Neural Network Technique for Cleaning Sleep Electroencephalograms
Vitaly Schetinin
Evolving Cascade Neural Networks (ECNNs) and a new training algorithm capable of selecting informative features are described. The ECNN initially learns with one input node and the…
Comparison of the Bayesian and Randomised Decision Tree Ensembles within an Uncertainty Envelope Technique
Vitaly Schetinin, Jonathan E. Fieldsend, Derek Partridge +4
Multiple Classifier Systems (MCSs) allow evaluation of the uncertainty of classification outcomes that is of crucial importance for safety critical applications. The uncertainty of…
Estimating Classification Uncertainty of Bayesian Decision Tree Technique on Financial Data
Vitaly Schetinin, Jonathan E. Fieldsend, Derek Partridge +4
Bayesian averaging over classification models allows the uncertainty of classification outcomes to be evaluated, which is of crucial importance for making reliable decisions in app…