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3 results for “regularization”

Formulae (3)

J = \text{MSE} + \lambda\sum_j w_j^2

Adds a penalty on squared weights to reduce overfitting.

J = \text{MSE} + \lambda\sum_j |w_j|

Penalizes absolute weights, driving many to exactly zero for feature selection.

\tilde{x} = \dfrac{x\odot m}{1-p}

Randomly zeroes activations during training to prevent co-adaptation.