On-line Update: Basic Algorithm

In some situations the data set is so huge that batch methods become impractical. In other cases the input data comes as a continuous stream of unlimited length which makes it completely impossible to apply batch methods. A resort is on-line update, which can be described as follows:

1.

Initialize the set to contain N units

with reference vectors chosen randomly according to .

2.

Generate at random an input signal according to .

3.

Determine the winner :

4.

Adapt the reference vector of the winner towards :

5.

Unless the maximum number of steps is reached continue with step 2.

Thereby, the learning rate determines the extent to which the winner is adapted towards the input signal. Depending on whether stays constant or decays over time, several different methods are possible some of which are described in the following.