Abstract Modeling a combinatorial problem is hard and error-prone task requiring significant expertise. Constraint acquisition methods attempt to automate this process by learning constraints from examples of solutions (usually) non-solutions. Active query an oracle while passive do not. We propose known but not widely-used application machine constraint acquisition: training classifier discrim...

Sleep stages classification is one of the most important methods for diagnosis in psychiatry and neurology. In this paper, a combination of three kinds of classifiers are proposed which classify the EEG signal into five sleep stages including Awake, N-REM (non-rapid eye movement) stage 1, N-REM stage 2, N-REM stage 3 and 4 (also called Slow Wave Sleep), and REM. Twenty-five all night recordings...

The Ensemble and mixture of expertise method is the most intuitive simple way to improve performance in field recognition using convolutional neural networks (CNNs). However, difficult apply real-time operation applications because amount computational overhead parameters increase proportion number models. In another, a that extracts various combines it cumbersome requires large change network....

The article suggests an algorithm for regular classifier ensemble methodology. The proposed methodology is based on possibilistic aggregation to classify samples. The argued method optimizes an objective function that combines environment recognition, multi-criteria aggregation term and a learning term. The optimization aims at learning backgrounds as solid clusters in subspaces of the high...