Concept drift robust adaptive novelty detection for data streams
M Cejnek, I Bukovsky - Neurocomputing, 2018 - Elsevier
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 …
detection methods (NDMs) on data with concept drift. Newly, the concept drift is considered …
Biomass combustion control in small and medium-scale boilers based on low cost sensing the trend of carbon monoxide emissions
The article deals with the possibility of efficient control of small and medium-scale biomass-
fired boilers by implementing low-cost sensors to sense the trend of carbon monoxide …
fired boilers by implementing low-cost sensors to sense the trend of carbon monoxide …
Learning entropy as a learning-based information concept
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 …
scale quantification of unusually large learning efforts of machine learning systems, was …
[HTML][HTML] AISLEX: Approximate individual sample learning entropy with JAX
O Budik, M Novak, F Sobieczky, I Bukovsky - SoftwareX, 2024 - Elsevier
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 …
algorithm, a novel machine learning-based information measure that quantifies the learning …
Influence of type and level of noise on the performance of an adaptive novelty detector
M Cejnek, I Bukovsky - 2017 IEEE 16th International …, 2017 - ieeexplore.ieee.org
This paper investigates the influence of the signal to noise ratio (SNR) and the type of a
noise on the performance of two adaptive novelty detection methods. The evaluated …
noise on the performance of two adaptive novelty detection methods. The evaluated …
Novelty detection in system monitoring and control with honu
Abstract With focus on Higher Order Neural Units (HONUs), this chapter reviews two recently
introduced adaptive novelty detection algorithms based on supervised learning of HONU …
introduced adaptive novelty detection algorithms based on supervised learning of HONU …
Parallelizing multiple linear regression for speed and redundancy: An empirical study
M Xu, JJ Miller, EJ Wegman - Proceedings of the Fifth Distributed …, 1990 - computer.org
This paper investigates the influence of the signal to noise ratio (SNR) and the type of a
noise on the performance of two adaptive novelty detection methods. The evaluated …
noise on the performance of two adaptive novelty detection methods. The evaluated …
Novelty Detection Via Linear Adaptive Filters
M Cejnek - 2020 - search.proquest.com
Novelty detection is an important signal processing task. This task is essential for many
industry, and biomedical applications. This thesis is presenting research on the topic of …
industry, and biomedical applications. This thesis is presenting research on the topic of …