regulariser

we added a regulariser to the loss function to prevent overfitting.

the regulariser term penalises large weights, encouraging simpler models.

choosing the right regulariser strength is crucial for model generalisation.

in deep learning, l2 regulariser is widely used to constrain network parameters.

a dropout layer can act as a regulariser, reducing reliance on specific neurons.

the researcher tuned the regulariser parameter using cross‑validation.

our approach combines a sparsity‑inducing regulariser with the main objective.

when training data is limited, a regulariser helps to stabilise the learning process.

the regulariser effect is more pronounced when the model capacity is high.

we compared several regularisers, including l1, l2, and elastic‑net.

the regulariser can be applied either to the input features or to the hidden units.

implementing a regulariser in the loss yields smoother decision boundaries.