International audienceThis paper presents CSOM, a Cellular Self-Organising Map which performs weight update in a cellular manner. Instead of updating weights towards new input vectors, it uses a signal propagation originated from the best matching unit to every other neuron in the network. Interactions between neurons are thus local and distributed. In this paper we present performance results showing than CSOM can obtain faster and better quantisation than classical SOM when used on high-dimensional vectors. We also present an application on video compression based on vector quantisation, in which CSOM outperforms SOM
Abstract Motivated by current attempts to use wireless in Brain-Machine Interfaces (BMIs), this pap...
Kohonen's Self Organizing Feature Map (SOFM) produces an ordered mapping from one space to another. ...
In this paper the basic principles and developments of an unsupervised learning algorithm, the Self-...
International audienceThis paper presents CSOM, a Cellular Self-Organising Map which performs weight...
International audienceSelf-organization is a bio-inspired feature that has been poorly develop...
A new rate-constrained self-organising map (SOM) learning algorithm, incorporating a noise-mixing mo...
Mapping quality of the self-organising maps (SOMs) is sensitive to the map topology and initialisati...
Self-organizing maps (SOM) are widely used for their topology preservation property: neighboring inp...
The Self-Organizing Map (SOM) has applications like dimension reduction, data clustering, image anal...
The internet age has fuelled an enormous explosion in the amount of information generated by humanit...
AbstractThe Self-Organizing Map (SOM) has applications like dimension reduction, data clustering, im...
AbstractWe present the Self-Organizing Map (SOM) is popular algorithm for unsupervised learning, whi...
The standard learning algorithm for self-organizing maps (SOM) involves the two steps of a search fo...
This paper introduces CSOM, a continuous version of the Self-Organizing Map (SOM). The CSOM network ...
International audienceSelf-Organizing Maps (SOM) are well-known unsupervised neural networks able to...
Abstract Motivated by current attempts to use wireless in Brain-Machine Interfaces (BMIs), this pap...
Kohonen's Self Organizing Feature Map (SOFM) produces an ordered mapping from one space to another. ...
In this paper the basic principles and developments of an unsupervised learning algorithm, the Self-...
International audienceThis paper presents CSOM, a Cellular Self-Organising Map which performs weight...
International audienceSelf-organization is a bio-inspired feature that has been poorly develop...
A new rate-constrained self-organising map (SOM) learning algorithm, incorporating a noise-mixing mo...
Mapping quality of the self-organising maps (SOMs) is sensitive to the map topology and initialisati...
Self-organizing maps (SOM) are widely used for their topology preservation property: neighboring inp...
The Self-Organizing Map (SOM) has applications like dimension reduction, data clustering, image anal...
The internet age has fuelled an enormous explosion in the amount of information generated by humanit...
AbstractThe Self-Organizing Map (SOM) has applications like dimension reduction, data clustering, im...
AbstractWe present the Self-Organizing Map (SOM) is popular algorithm for unsupervised learning, whi...
The standard learning algorithm for self-organizing maps (SOM) involves the two steps of a search fo...
This paper introduces CSOM, a continuous version of the Self-Organizing Map (SOM). The CSOM network ...
International audienceSelf-Organizing Maps (SOM) are well-known unsupervised neural networks able to...
Abstract Motivated by current attempts to use wireless in Brain-Machine Interfaces (BMIs), this pap...
Kohonen's Self Organizing Feature Map (SOFM) produces an ordered mapping from one space to another. ...
In this paper the basic principles and developments of an unsupervised learning algorithm, the Self-...