In this study, we propose a modified SOM which calculate the distance from input data and find the nearest neuron among neighborhood of winner neuron (BMU). It ...
Oct 22, 2024 · In this paper, we have divided the neurons on the map into two groups according to distance from input: normal and distant. The neurons which ...
The SOM map consists of a one or two dimensional (2-D) grid of nodes. These nodes are also called neurons. Each neuron's weight vector has the same dimension as ...
A novel Self-Organizing Map (SOM) learning algorithm with nearest and farthest neurons · Computer Science · 2014.
We investigate the behavior of modified SOM in detail. The learning performance is evaluated using the three measurements. We apply modified SOM to various ...
May 8, 2013 · In this paper, we have divided the neurons on the map into two groups according to distance from input: normal and distant. The neurons which ...
Missing: nearest | Show results with:nearest
The Self-Organizing Map (SOM) has applications like dimension reduction, data clustering, image analysis, and many others. In conventional SOM, the weights ...
People also ask
What is a self-organizing map in a neural network?
What is a self organizing map for data classification?
Why use self organizing maps?
Under which of the following do self-organizing maps lie?
An efficient Self-organzing map algorithm using group of neurons, divided according to distance from input, and compared using three well known parameters ...
Missing: nearest | Show results with:nearest
A novel Self-Organizing Map (SOM) learning algorithm with nearest and farthest neurons ... The use of weighted self-organizing maps to interrogate large seismic ...
Oct 23, 2014 · Abstract The Self-Organizing Map (SOM) has applications like dimension reduction, data cluster- ing, image analysis, and many others.