Parameter prediction is essential for many applications, facilitating insightful interpretation
and decision-making. However, in many real life domains, such as power systems …

We propose in this paper a new learning algorithm probabilistic self-organizing map
(PRSOM) using a probabilistic formalism for topological maps. This algorithm approximates …

In order to extract useful information from data streams, incremental learning has been
introduced in more and more data mining algorithms. For instance, a self-organizing …

Self-organizing neural networks are usually focused on prototype learning, while the
topology is held fixed during the learning process. Here a method to adapt the topology of …

In this work, we focus on the problem of training ensembles or, more generally, a set of self-
organizing maps (SOMs). In the light of new theory behind ensemble learning, in particular …

This study proposes a Batch-Learning Self-Organizing Map with Weighted Connections
avoiding false-neighbor effects (BL-WCSOM). We apply BL-WCSOM to several high …

We utilize classical facts from topology to show that the classification problem in machine
learning is always solvable under very mild conditions. Furthermore, we show that a softmax …

Merge trees are a valuable tool in scientific visualization of scalar fields; however, current
methods for merge tree comparisons are computationally expensive, primarily due to the …

One of the main drawbacks of the practical use of neural networks is the long time required
in the training process. Such a training process consists of an iterative change of parameters …

Hierarchical topological structure learning methods are expected to be developed in the
field of data mining for extracting multiscale topological structures from an unknown dataset …