Prototype‐based models in machine learning

M Biehl, B Hammer, T Villmann - … Reviews: Cognitive Science, 2016‏ - Wiley Online Library
An overview is given of prototype‐based models in machine learning. In this framework,
observations, ie, data, are stored in terms of typical representatives. Together with a suitable …

Intelligent support in manufacturing process selection based on artificial neural networks, fuzzy logic, and genetic algorithms: Current state and future perspectives

F Mumali, J Kałkowska - Computers & Industrial Engineering, 2024‏ - Elsevier
Technological advances, dynamic customer needs, growing uncertainty, and the imperative
for sustainable development pressure manufacturing entities to enhance productivity and …

Urine steroid metabolomics as a biomarker tool for detecting malignancy in adrenal tumors

W Arlt, M Biehl, AE Taylor, S Hahner… - The Journal of …, 2011‏ - academic.oup.com
Context: Adrenal tumors have a prevalence of around 2% in the general population.
Adrenocortical carcinoma (ACC) is rare but accounts for 2–11% of incidentally discovered …

A review of learning vector quantization classifiers

D Nova, PA Estévez - Neural Computing and Applications, 2014‏ - Springer
In this work, we present a review of the state of the art of learning vector quantization (LVQ)
classifiers. A taxonomy is proposed which integrates the most relevant LVQ approaches to …

On the computation of counterfactual explanations--A survey

A Artelt, B Hammer - arxiv preprint arxiv:1911.07749, 2019‏ - arxiv.org
Due to the increasing use of machine learning in practice it becomes more and more
important to be able to explain the prediction and behavior of machine learning models. An …

Supervised vessel delineation in retinal fundus images with the automatic selection of B-COSFIRE filters

N Strisciuglio, G Azzopardi, M Vento… - Machine Vision and …, 2016‏ - Springer
The inspection of retinal fundus images allows medical doctors to diagnose various
pathologies. Computer-aided diagnosis systems can be used to assist in this process. As a …

SurvivalLVQ: Interpretable supervised clustering and prediction in survival analysis via Learning Vector Quantization

J de Boer, K Dedja, C Vens - Pattern Recognition, 2024‏ - Elsevier
Identifying subgroups with similar survival outcomes is a pivotal challenge in survival
analysis. Traditional clustering methods often neglect the outcome variable, potentially …

Feature quantization improves gan training

Y Zhao, C Li, P Yu, J Gao, C Chen - arxiv preprint arxiv:2004.02088, 2020‏ - arxiv.org
The instability in GAN training has been a long-standing problem despite remarkable
research efforts. We identify that instability issues stem from difficulties of performing feature …

Regularization in matrix relevance learning

P Schneider, K Bunte, H Stiekema… - … on Neural Networks, 2010‏ - ieeexplore.ieee.org
In this paper, we present a regularization technique to extend recently proposed matrix
learning schemes in learning vector quantization (LVQ). These learning algorithms extend …

Learning vector quantization for (dis-) similarities

B Hammer, D Hofmann, FM Schleif, X Zhu - Neurocomputing, 2014‏ - Elsevier
Prototype-based methods often display very intuitive classification and learning rules.
However, popular prototype based classifiers such as learning vector quantization (LVQ) are …