Recursive learning rules for SOMs
نویسندگان
چکیده
Self-Organizing Maps (SOMs, [1,2]) are well known in the domain of Vector Quantization (VQ). Unlike other VQ methods, the neurons (or prototypes) used for the quantization are given a position in a grid, which is often oneor two-dimensional. This predefined geometrical organization, combined with a well chosen learning rule, generates a self-organizing behavior, useful in numerous areas like nonlinear projection and data representation. More technically, learning rules for VQ can be classified into two sets, according to the number of neurons which are adapted at each stimulation of the network:
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