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The artificial neural network introduced by the Finnish professor Teuvo Kohonen in the 1980s is sometimes called a Kohonen map or network.Like most artificial neural networks, SOMs operate in two modes: training and mapping.T-1, then repeat, T being the training sample's size), be randomly drawn from the data set (bootstrap sampling), or implement some other sampling method (such as jackknifing).The neighborhood function Θ(u, v, s) depends on the lattice distance between the BMU (neuron u) and neuron v.The blue blob is the distribution of the training data, and the small white disc is the current training datum drawn from that distribution.
The usual arrangement of nodes is a two-dimensional regular spacing in a hexagonal or rectangular grid.
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The examples are usually administered several times as iterations. When a training example is fed to the network, its Euclidean distance to all weight vectors is computed.
The neuron whose weight vector is most similar to the input is called the best matching unit (BMU).