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 …

Efficient learning of a data analysis task strongly depends on the data representation. Most
methods rely on (symmetric) similarity or dissimilarity representations by means of metric …

In many fields, superior gains have been obtained by leveraging the computational power of
machine learning techniques to solve expert tasks. In this paper we present an application of …

Viral diseases are major sources of poor yields for cassava, the 2nd largest provider of
carbohydrates in Africa. At least 80% of small-holder farmer households in Sub-Saharan …

We present a systematic approach to the mathematical treatment of the t-distributed
stochastic neighbor embedding (t-SNE) and the stochastic neighbor embedding (SNE) …

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

We discuss the use of matrix relevance learning, a popular extension to prototype learning
algorithms, applied to a three-class classification task of diagnosing cassava diseases from …

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 …

Abstract Domain specific (dis-) similarity or proximity measures used eg in alignment
algorithms of sequence data are popular to analyze complicated data objects and to cover …

Prototype-based models like the Generalized Learning Vector Quantization (GLVQ) belong
to the class of interpretable classifiers. Moreover, quantum-inspired methods get more and …