This work investigates the self-organizing representation of temporal data in prototypebased
neural networks. Extensions of the supervised learning vector quantization (LVQ) and the …

The self-organizing map (SOM) is a valuable tool for data visualization and data mining for
potentially high-dimensional data of an a priori fixed dimensionality. We investigate SOMs …

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

Self-organization constitutes an important paradigm in machine learning with successful
applications eg for data-and web-mining. However, so far most approaches have been …

Analyzing sequences of continuous data is an important step in perception. It has been
shown that extensions of the self-organizing map (SOM) learn temporal dynamics. Here, the …

We examine the extent to which modified Kohonen self-organizing maps (SOMs) can learn
unique representations of temporal sequences while still supporting map formation. Two …

We present a novel approach to unsupervised temporal sequence processing in the form of
an unsupervised, recurrent neural network based on a self-organizing map (SOM). A …

Concerns neural-based modeling of symbolic chaotic time series. We investigate the
knowledge induction process associated with training recurrent mural nets (RNN) on single …

A self-organizing neural net for learning and recall of complex temporal sequences is
developed and applied to robot trajectory planning. We consider trajectories with both …

Self-organization constitutes an important paradigm in machine learning with successful
applications eg in data-and web-mining. Most approaches, however, have been proposed …