This paper presents a comprehensive overview of modelling, simulation and implementation
of neural networks, taking into account that two aims have emerged in this area: the …

The self-organizing map (SOM) is an automatic data-analysis method. It is widely applied to
clustering problems and data exploration in industry, finance, natural sciences, and …

Praise for the Second Edition:" The authors present an intuitive and easy-to-read book.…
accompanied by many examples, proposed exercises, good references, and …

Learning from imbalanced datasets is challenging for standard algorithms, as they are
designed to work with balanced class distributions. Although there are different strategies to …

Abstract The Self-Organizing Map (SOM) algorithm has attracted a great deal of interest
among researches and practitioners in a wide variety of fields. The SOM has been analyzed …

Abstract The Self-Organizing Map (SOM) algorithm has attracted an ever increasing amount
of interest among researches and practitioners in a wide variety of elds. The SOM and a …

The self-organizing map (SOM) represents an open set of input samples by a topologically
organized, finite set of models. In this paper, a new version of the SOM is used for the …

This paper explores the combination of self-organizing map (SOM) and feedback, in order to
represent sequences of inputs. In general, neural networks with time-delayed feedback …

Publisher Summary By changing the definition of the winning unit, Kohonen's original
learning rule can be viewed as performing stochastic gradient descent on an energy …

The Self-Organizing Map, or Kohonen Map, is one of the most widely used neural network
algorithms, with thousands of applications covered in the literature. It was one of the strong …