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

Technological advances, dynamic customer needs, growing uncertainty, and the imperative
for sustainable development pressure manufacturing entities to enhance productivity and …

In this paper, we introduce taxonomies for similarity and dissimilarity measures, respectively,
based on their mathematical properties. Further, we propose a definition for rank …

Metric learning has the aim to improve classification accuracy by learning a distance
measure which brings data points from the same class closer together and pushes data …

In this paper, we propose a general model to address the overfitting problem in online
similarity learning for big data, which is generally generated by two kinds of redundancies …

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 …

Classification by means of machine learning models constitutes one relevant technology in
process automation and predictive maintenance. However, common techniques such as …

Abstract The Shallow and the Deep is a collection of lecture notes that offers an accessible
introduction to neural networks and machine learning in general. However, it was clear from …

We present an effective method for supervised landmark selection in sparse Nyström
approximations of kernel matrices for structured data. Our approach transforms structured …

We propose a learning vector quantization algorithm variant for prototype-based
classification learning with adaptive tangent distance learning. Tangent distances were …