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 …

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 …

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 …

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 …

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 …

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 …

In this paper, we present a regularization technique to extend recently proposed matrix
learning schemes in learning vector quantization (LVQ). These learning algorithms extend …

Identifying subgroups with similar survival outcomes is a pivotal challenge in survival
analysis. Traditional clustering methods often neglect the outcome variable, potentially …

Prototype-based methods often display very intuitive classification and learning rules.
However, popular prototype based classifiers such as learning vector quantization (LVQ) are …

Classification is one of the most frequent tasks in machine learning. However, the variety of
classification tasks as well as classifier methods is huge. Thus the question is coming up …