SANN - Custom Neural Network/Subsampling - Weight Decay Tab
You can select the Weight Decay tab of the SANN - Custom Neural Network dialog box or the SANN - Subsampling dialog box to access the options described here. For information on the options that are common to all tabs (located at the top and on the lower-right side of the dialog box), see SANN - Custom Neural Network or SANN - Subsampling.
Option | Description |
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Weight decay | Use the options in this group box to specify the use of weight decay regularization for the input-hidden layer (MLP networks only), the hidden-output layer, or both. This option encourages the development of smaller weights, which tends to reduce the problem of over-fitting, thereby potentially improving generalization performance of the network. Weight decay works by modifying the network's error function to penalize large weights - the result is an error function that compromises between performance and weight size. Consequently, too large a weight decay term may damage network performance unacceptably, and experimentation is generally needed to determine an appropriate weight decay factor for a particular problem domain.
Note: When the
Radial basis functions (RBF) option button is selected on the
Quick (MLP/RBF) tab, the
Use hidden weight decay check box and
Decay value field is unavailable.
|
Use hidden weight decay | Select this check box to apply weight decay regularization to the input-hidden layer weights. |
Decay value | Specifies the value for the hidden layer weights. The larger the decay value the weaker the network. |
Use output weight decay | Select this check box to apply weight decay regularization to the hidden-output layer weights. |
Decay value | Specifies the weight decay value for the output layer weights. The larger the decay value the weaker the network. |
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