In this paper, we propose an elegant solution that is directly addressing the bottlenecks of
the traditional deep learning approaches and offers an explainable internal architecture that …

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 …

Learning vector quantization (LVQ) is one of the most powerful approaches for prototype
based classification of vector data, intuitively introduced by Kohonen. The prototype …

In this study, a precise and efficient eigenvalue-based machine learning algorithm,
particularly denoted as Eigenvalue Classification (EigenClass) algorithm, has been …

The convolution operator at the core of many modern neural architectures can effectively be
seen as performing a dot product between an input matrix and a filter. While this is readily …

The encounter of large amounts of biological sequence data generated during the last
decades and the algorithmic and hardware improvements have offered the possibility to …

We present an approach to efficiently embed complex data objects from the chem-and
bioinformatics domain like graph structures into Euclidean vector spaces such that those …

Dropout and DropConnect are useful methods to prevent multilayer neural networks from
overfitting. In addition, it turns out that these tools can also be used to estimate the stability of …

Neural networks currently dominate the machine learning community and they do so for
good reasons. Their accuracy on complex tasks such as image classification is unrivaled at …

The advantage of prototype based learning vector quantizers are the intuitive and simple
model adaptation as well as the easy interpretability of the prototypes as class …