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How is weight regularization used to penalize a model?
Rather than adding each weight to the penalty directly, they can be weighted using a new hyperparameter called alpha (a) or sometimes lambda. This controls the amount of attention that the learning process should pay to the penalty. Or put another way, the amount to penalize the model based on the size of the weights.
How does weight regularization work to reduce overfitting?
Larger weights result in a larger penalty, in the form of a larger loss score. The optimization algorithm will then push the model to have smaller weights, i.e. weights no larger than needed to perform well on the training dataset.
How is weight regularization used in optimization algorithms?
The optimization algorithm will then push the model to have smaller weights, i.e. weights no larger than needed to perform well on the training dataset. Smaller weights are considered more regular or less specialized and as such, we refer to this penalty as weight regularization.
When to use weight regularization in neural networks?
Weight regularization is a generic approach. It can be used with most, perhaps all, types of neural network models, not least the most common network types of Multilayer Perceptrons, Convolutional Neural Networks, and Long Short-Term Memory Recurrent Neural Networks.
Why is weight regularization used in machine learning?
This particular choice of regularizer is known in the machine learning literature as weight decay because in sequential learning algorithms, it encourages weight values to decay towards zero, unless supported by the data. In statistics, it provides an example of a parameter shrinkage method because it shrinks parameter values towards zero.
When to regularize to reduce the model space?
It isn’t so much having small weights that prevents overfitting (I think), it is more the fact that regularizing more strongly reduces the model space. In fact you can regularize around 10000000 if you wanted to by taking the L2 norm of your X values minus a vector of 10000000s.