The adaptive processing of graph data is a long-standing research topic that has been lately
consolidated as a theme of major interest in the deep learning community. The snap …

An overview is given of prototype‐based models in machine learning. In this framework,
observations, ie, data, are stored in terms of typical representatives. Together with a suitable …

The chain-structured long short-term memory (LSTM) has showed to be effective in a wide
range of problems such as speech recognition and machine translation. In this paper, we …

A topographic map is a two-dimensional, nonlinear approximation of a potentially high-
dimensional data manifold, which makes it an appealing instrument for visualizing and …

The recent merging self-organizing map (MSOM) for unsupervised sequence processing
constitutes a fast, intuitive, and powerful unsupervised learning model. In this paper, we …

Research on integrated neural-symbolic systems has made significant progress in the
recent past. In particular the understanding of ways to deal with symbolic knowledge within …

In recent years, Deep Learning methods and architectures have reached impressive results,
allowing quantum-leap improvements in performance in many difficult tasks, such as speech …

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 …

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 the paper, an overview of methods and technologies used for big data clustering is
presented. The clustering is one of the important data mining issue especially for big data …