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 …

Learning vector quantization applying non-standard metrics became quite popular for
classification performance improvement compared to standard approaches using the …

This article studies constructions of reproducing kernel Banach spaces (RKBSs) which may
be viewed as a generalization of reproducing kernel Hilbert spaces (RKHSs). A key point is …

The basic concepts of distance based classification are introduced in terms of clear-cut
example systems. The classical k-Nearest-Neigbhor (kNN) classifier serves as the starting …

In some applications and in order to address real-world situations better, data may be more
complex than simple numerical vectors. In some examples, data can be known only through …

Prototype based vector quantization is usually proceeded in the Euclidean data space. In
the last years, also non-standard metrics became popular. For classification by support …

The contribution describes our application to the ESANN'2013 Competition on Human
Activity Recognition (HAR) using Android-OS smartphone sensor signals. We applied a …

Learning vector quantization (LVQ) algorithms as powerful classifier models for class
discrimination of vectorial data belong to the family of prototype-based classifiers with a …

In the present paper we investigate the application of differentiable kernel for generalized
matrix learning vector quantization as an alternative kernel-based classifier, which …

We present a novel approach for support vector machines in reproducing kernel Banach
spaces induced by a finite basis. In particular, we show that the support vector classification …