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Kohonen rule

The winning neuron Ws and the neighboring vectors are then modified according to the following formula (Kohonen rule) ... [Pg.57]

Now, one may ask, what if we are going to use Feed-Forward Neural Networks with the Back-Propagation learning rule Then, obviously, SVD can be used as a data transformation technique. PCA and SVD are often used as synonyms. Below we shall use PCA in the classical context and SVD in the case when it is applied to the data matrix before training any neural network, i.e., Kohonen s Self-Organizing Maps, or Counter-Propagation Neural Networks. [Pg.217]


See other pages where Kohonen rule is mentioned: [Pg.343]    [Pg.343]    [Pg.687]    [Pg.692]    [Pg.264]    [Pg.123]    [Pg.484]    [Pg.346]    [Pg.176]    [Pg.165]    [Pg.26]    [Pg.43]    [Pg.43]    [Pg.688]    [Pg.342]    [Pg.193]    [Pg.51]    [Pg.4549]    [Pg.124]    [Pg.2039]    [Pg.386]    [Pg.219]    [Pg.166]    [Pg.30]    [Pg.207]    [Pg.414]   
See also in sourсe #XX -- [ Pg.83 ]




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