The coming of age of interpretable and explainable machine learning models
Abstract Machine-learning-based systems are now part of a wide array of real-world
applications seamlessly embedded in the social realm. In the wake of this realization, strict …
applications seamlessly embedded in the social realm. In the wake of this realization, strict …
Reactive soft prototype computing for concept drift streams
C Raab, M Heusinger, FM Schleif - Neurocomputing, 2020 - Elsevier
The amount of real-time communication between agents in an information system has
increased rapidly since the beginning of the decade. This is because the use of these …
increased rapidly since the beginning of the decade. This is because the use of these …
Quantum computing approaches for vector quantization—current perspectives and developments
A Engelsberger, T Villmann - Entropy, 2023 - mdpi.com
In the field of machine learning, vector quantization is a category of low-complexity
approaches that are nonetheless powerful for data representation and clustering or …
approaches that are nonetheless powerful for data representation and clustering or …
Quality 4.0 in action: smart hybrid fault diagnosis system in plaster production
J Ramezani, J Jassbi - Processes, 2020 - mdpi.com
Industry 4.0 (I4. 0) represents the Fourth Industrial Revolution in manufacturing, expressing
the digital transformation of industrial companies employing emerging technologies …
the digital transformation of industrial companies employing emerging technologies …
Classification-by-components: Probabilistic modeling of reasoning over a set of components
Neural networks are state-of-the-art classification approaches but are generally difficult to
interpret. This issue can be partly alleviated by constructing a precise decision process …
interpret. This issue can be partly alleviated by constructing a precise decision process …
Quantum-inspired learning vector quantizers for prototype-based classification: Confidential: for personal use only—submitted to Neural Networks and Applications 5 …
Prototype-based models like the Generalized Learning Vector Quantization (GLVQ) belong
to the class of interpretable classifiers. Moreover, quantum-inspired methods get more and …
to the class of interpretable classifiers. Moreover, quantum-inspired methods get more and …
Learning vector quantization as an interpretable classifier for the detection of SARS-CoV-2 types based on their RNA sequences
M Kaden, KS Bohnsack, M Weber, M Kudła… - Neural Computing and …, 2022 - Springer
We present an approach to discriminate SARS-CoV-2 virus types based on their RNA
sequence descriptions avoiding a sequence alignment. For that purpose, sequences are …
sequence descriptions avoiding a sequence alignment. For that purpose, sequences are …
Domain adversarial tangent subspace alignment for explainable domain adaptation
C Raab, M Röder, FM Schleif - Neurocomputing, 2022 - Elsevier
Deep learning is reaching state of the art in many applications. However, the generalization
capabilities of the learned networks are limited to the training or source domain. The …
capabilities of the learned networks are limited to the training or source domain. The …
Online semi-supervised learning with learning vector quantization
Online semi-supervised learning (OSSL) is a learning paradigm simulating human learning,
in which the data appear in a sequential manner with a mixture of both labeled and …
in which the data appear in a sequential manner with a mixture of both labeled and …
Leakage detection in water distribution networks using machine-learning strategies
This work proposes a reliable leakage detection methodology for water distribution networks
(WDNs) using machine-learning strategies. Our solution aims at detecting leakage in WDNs …
(WDNs) using machine-learning strategies. Our solution aims at detecting leakage in WDNs …