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Self-organizing maps advantage

The Kohonen network or self-organizing map (SOM) was developed by Teuvo Kohonen [11]. It can be used to classify a set of input vectors according to their similarity. The result of such a network is usually a two-dimensional map. Thus, the Kohonen network is a method for projecting objects from a multidimensional space into a two-dimensional space. This projection keeps the topology of the multidimensional space, i.e., points which are close to one another in the multidimensional space are neighbors in the two-dimensional space as well. An advantage of this method is that the results of such a mapping can easily be visualized. [Pg.456]


See other pages where Self-organizing maps advantage is mentioned: [Pg.556]    [Pg.347]    [Pg.365]    [Pg.317]    [Pg.348]    [Pg.26]    [Pg.164]    [Pg.364]    [Pg.579]    [Pg.601]    [Pg.601]    [Pg.142]    [Pg.89]    [Pg.5]   
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