Adaptive Norm-Based Regularization for Neural Networks
In this paper, we study norm-based regularization methods for neural networks. We compare existing penalization approaches and introduce two regularization strategies that extend classical ridge- and lasso-type penalties to neural network models. The first strategy modifies weight decay by incorporating the covariance structure of the input features into a ridge-type $\ell_2$ penalty, allowing regularization to account for feature dependence. The second combines an $\ell_1$ sparsity penalty with covariance-aware $\ell_2$ regularization, producing neural network weights that are both sparse and structurally informed. Monte Carlo simulations are used to evaluate these methods under differen...
| Authors | Muhammad Qasim, Farrukh Javed |
|---|---|
| Primary category | stat.ML |
| Published | 30 April 2026 |
| Last updated at source | 30 April 2026 |
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