Graph-structured data appears frequently in domains including chemistry, natural language
semantics, social networks, and knowledge bases. In this work, we study feature learning …

In this overview, commonly used dimensionality reduction techniques for data visualization
and their properties are reviewed. Thereby, the focus lies on an intuitive understanding of …

Many underlying relationships among data in several areas of science and engineering, eg,
computer vision, molecular chemistry, molecular biology, pattern recognition, and data …

This paper presents a new approach for learning in structured domains (SDs) using a
constructive neural network for graphs (NN4G). The new model allows the extension of the …

The soaring amount of data, especially spatial-temporal data, recorded in recent years
demands for advanced analysis methods. Neural networks derived from self-organizing …

Self-organizing models constitute valuable tools for data visualization, clustering, and data
mining. Here, we focus on extensions of basic vector-based models by recursive …

In this paper we present the Tree Echo State Network (TreeESN) model, generalizing the
paradigm of Reservoir Computing to tree structured data. TreeESNs exploit an untrained …

We describe approaches for positive data modeling and classification using both finite
inverted Dirichlet mixture models and support vector machines (SVMs). Inverted Dirichlet …

The soaring amount of data, especially spatial-temporal data, recorded in recent years
demands for advanced analysis methods. Neural networks derived from self-organizing …

Intelligent tutoring systems (ITSs) typically rely on a formalised model of the underlying
domain knowledge in order to provide feedback to learners adaptively to their needs. This …