Open AccessBook
Self-Organizing neural networks: recent advances and applications
Udo Seiffert,Lakhmi C. Jain +1 more
TLDR
A compact and vivid collection of almost all aspects of current research on Self-Organizing Maps, ranging from theoretical work, several technical and non-technical applications, numerical and implementation details on sequential and parallel hardware to self-organisation with spiking neurons.Abstract:
This compilation contains new theoretical work as well as up-to-date applications contributed by some of the world leaders of current SOM research. It is a compact and vivid collection of almost all aspects of current research on Self-Organizing Maps, ranging from theoretical work, several technical and non-technical applications, numerical and implementation details on sequential and parallel hardware to self-organisation with spiking neurons. An overture, given by Teuvo Kohonen, builds a bridge from the development of the fundamentals to the many extensions, modifications and applications which have made this neural network architecture so successful.read more
Citations
More filters
Journal ArticleDOI
Essentials of the self-organizing map
TL;DR: The self-organizing map (SOM) is an automatic data-analysis method widely applied to clustering problems and data exploration in industry, finance, natural sciences, and linguistics and can be found in the management of massive textual databases and in bioinformatics.
Bibliography of Self-Organizing Map SOM) Papers: 1998-2001 Addendum
TL;DR: This work has provided a keyword index to help finding articles of interest, and additionally a modern automatically constructed variant of a thematic index: a WEBSOM interface to the whole article collection of years 1981-2000.
Journal ArticleDOI
From unknown sensors and actuators to actions grounded in sensorimotor perceptions
TL;DR: A developmental system based on information theory implemented on a real robot that learns a model of its own sensory and actuator apparatus and can perform basic visually guided movement.
Journal ArticleDOI
Assessment of applicability domain for multivariate counter-propagation artificial neural network predictive models by minimum euclidean distance space analysis: a case study.
TL;DR: A novel, simple, and effective distance-based method for estimation of the AD in case of developed and validated predictive predictive counter-propagation artificial neural network (CP ANN) models through a proficient exploitation of the euclidean distance (ED) metric in the structure-representation vector space is introduced.
Journal ArticleDOI
Artificial neural networks on massively parallel computer hardware
Udo Seiffert,Udo Seiffert +1 more
TL;DR: A survey of the state-of-the-art parallel computer hardware from a neural networks user's point of view and guides those people who are willing to go the way of a parallel implementation utilising the most recent and accessible parallelComputer hardware and software are given.