We propose a new matrix learning scheme to extend relevance learning vector quantization
(RLVQ), an efficient prototype-based classification algorithm, toward a general adaptive …

We propose a new scheme for enlarging generalized learning vector quantization (GLVQ)
with weighting factors for the input dimensions. The factors allow an appropriate scaling of …

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 …

We study the application of self-organizing maps (SOMs) for the analyses of remote sensing
spectral images. Advanced airborne and satellite-based imaging spectrometers produce …

We present an extension of the recently introduced Generalized Matrix Learning Vector
Quantization algorithm. In the original scheme, adaptive square matrices of relevance …

Prototype based classification offers intuitive and sparse models with excellent
generalization ability. However, these models usually crucially depend on the underlying …

Discriminative vector quantization schemes such as learning vector quantization (LVQ) and
extensions thereof offer efficient and intuitive classifiers based on the representation of …

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

We investigate the extraction of effective color features for a content-based image retrieval
(CBIR) application in dermatology. Effectiveness is measured by the rate of correct retrieval …

We derive a generalization bound for prototype-based classifiers with adaptive metric. The
bound depends on the margin of the classifier and is independent of the dimensionality of …