Abstract. In this paper we consider the question of whether it is possible to classify n-back EEG data into different memory loads across subjects. To capture relevant information ...
Deep autoencoder networks have successfully been applied in unsupervised dimension reduction. The autoencoder has a "bottleneck" middle layer of only a few hidden units, ...
In the realm of multilabel classification (MLC), it has become an opinio communis that optimal predictive performance can only be achieved by learners that explicitly take label d...
This paper presents a classification approach, where a sample is represented by a set of feature vectors called an attributed point pattern. Some attributes of a point are transf...
Abstract. Building on the celebrated Krohn-Rhodes Theorem we characterize classes of regular languages in terms of the cascade decompositions of minimal DFA of languages in those c...