On two recent nonconvex penalties for regularization in machine learning
On two recent nonconvex penalties for regularization in machine learning
Blog Article
Regularization methods are often employed to reduce overfitting of machine learning citronella horse shampoo models.Nonconvex penalty functions are often considered for regularization because of their near-unbiasedness properties.In this paper, we consider two relatively new penalty functions: Laplace and arctan, and show how they fit into certain recently introduced statistical and optimization frameworks.We also compare empirically the performance of the two new penalty functions with existing penalty functions utilized as regularizers of deep neural networks and convolutional neural networks on read more seven different datasets.