Alzheimer's disease is diagnosed via means of daily activity assessment. The EEG
recording evaluation is a supporting tool that can assist the practitioner to recognize the …

In this paper we study the performance of two original adaptive unsupervised novelty
detection methods (NDMs) on data with concept drift. Newly, the concept drift is considered …

Stability evaluation of a weight-update system of higher order neural units (HONUs) with
polynomial aggregation of neural inputs (also known as classes of polynomial neural …

Recently, a novel concept of a non-probabilistic novelty detection measure, based on a multi-
scale quantification of unusually large learning efforts of machine learning systems, was …

We present AISLEX, an online anomaly detection module based on the Learning Entropy
algorithm, a novel machine learning-based information measure that quantifies the learning …

The paper presents new approach to dementia detection in time series of measured EEG.
The proposed method introduced in this paper evaluates EEG signal according to included …

This paper presents a case study of non-Shannon entropy, ie Learning Entropy (LE), for
instant detection of onset of epileptic seizures in individual EEG time series. Contrary to …

Deep Learning has a hierarchical network architecture to represent the complicated feature
of input patterns. The adaptive structural learning method of Deep Belief Network (Adaptive …

This paper deals with the case study of usability of the Learning Entropy approach for the
adaptive novelty detection in MIMO dynamical systems. The novelty detection is studied for …

This chapter is a summarizing study of Higher Order Neural Units featuring the most
common learning algorithms for identification and adaptive control of most typical …