DLVQ in SNNS

To start DLVQ, the learning function DLVQ, the update function DLVQ_Update, and the init function DLVQ_Weights have to be selected in the corresponding menus. The init functions of DLVQ differ a little from the normal function: if a DLVQ net is initialized, all hidden units are deleted.

As with learning rules CC and RCC the text field CYCLE in the control panel does not specify the number of learning cycles. This field is used to specify the maximal number of class units to be generated for each class during learning. The number of learning cycles is entered as the third parameter in the control panel (see below).

1. : learning rate, specifies the step width of the mean vector , which is nearest to a pattern , towards this pattern. Remember that is moved only, if is not assigned to the correct class . A typical value is 0.03.

2. : learning rate, specifies the step width of a mean vector , to which a pattern of class is falsely assigned to, away from this pattern. A typical value is 0.03. Best results can be achieved, if the condition is satisfied.

3. Number of cycles you want to train the net before additive mean vectors are calculated.

If the topology of a net fits to the DLVQ architecture SNNS will order the units and layers from left to right independent in the following way: input layer, hidden layer output layer. The hidden layer itself is ordered by classes.

The output layer must consist of only one unit. At the start of the learning phase it does not matter whether the output layer and the input layer are connected. If hidden units exist, they are fully connected with the input layer. The links between these layers contain the values of the the mean vectors. The output layer and the hidden layer are fully connected. All these links have the value 1 assigned.

  
Figure: Topology of a net which was trained with DLVQ.

The output pattern contains information on which class the input pattern belongs to. The lowest class must have the name 0. If there are n classes, the n-th class has the name n-1. If these conditions are violated, an error occurs. Figure shows the topology of a net. In the bias of every class unit its class name is stored. It can be retrieved by clicking on a class unit with right mouse button.

Note: In the first implementation of DLVQ the input patterns were automatically normalized by the algorithm. This step was eliminated, since is produced undesired behavior in some cases. Now the user has to take all necessary steps to normalize the input vectors correctly before loading them into SNNS.