|  | ## Self-Organizing Map

#### Synopsis

Trains a self-organizing map and applyes the examples on the map. The resulting coordinates are used as new attributes.

#### Description

This operator performs a dimensionality reduction based on a SOM (Self Organizing Map, aka Kohonen net).

#### Input

- example set input: expects: ExampleSet, expects: ExampleSet

#### Output

- example set output:
- original:
- preprocessing model:

#### Parameters

- return preprocessing model: Indicates if the preprocessing model should also be returned
- number of dimensions: Defines the number of dimensions, the data shall be reduced.
- net size: Defines the size of the SOM net, by setting the length of every edge of the net.
- training rounds: Defines the number of trainnig rounds
- learning rate start: Defines the strength of an adaption in the first round. The strength will decrease every round until it reaches the learning_rate_end in the last round.
- learning rate end: Defines the strength of an adaption in the last round. The strength will decrease to this value in last round, beginning with learning_rate_start in the first round.
- adaption radius start: Defines the radius of the sphere around an stimulus, within an adaption occoures. This radius decreases every round, starting by adaption_radius_start in first round, to adaption_radius_end in last round.
- adaption radius end: Defines the radius of the sphere around an stimulus, within an adaption occoures. This radius decreases every round, starting by adaption_radius_start in first round, to adaption_radius_end in last round.

#### ExampleProcess