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

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 present an extension of the Exploratory Observation Machine (XOM) for structure-
preserving dimensionality reduction. Based on minimizing the Kullback–Leibler divergence …

Due to the tremendous increase of electronic information with respect to the size of data sets
as well as their dimension, dimension reduction and visualization of high-dimensional data …

OBJECTIVE: The generalized matrix learning vector quantization (GMLVQ) is used to
estimate the relevance of texture features in their ability to classify interstitial lung disease …

In this chapter, one of themost popular and intuitive prototype-based classification
algorithms, learning vector quantization (LVQ), is revisited, and recent extensions towards …

Generalized Matrix Learning Vector Quantization (GMLVQ) critically relies on the use of an
optimization algorithm to train its model parameters. We test various schemes for automated …

Mijn thesis presenteert een aantal extensies van het Learning Vector Quantization algoritme
gebaseerd op het concept van adaptive similarity measures. Deze manier van metric …

We investigate the convergence properties of heuristic matrix relevance updates in Learning
Vector Quantization. Under mild assumptions on the training process, stationarity conditions …