GitHub

L1-vs-L2-Regularization-in-Machine-Learning-with-Code-Examples

A hands-on tutorial to understand L1 (Lasso) and L2 (Ridge) regularization using Python and Scikit-learn with visual and performance comparison. This repository provides a detailed and practical ...
IEEE

Source-Free Semantic Regularization Learning for Semi-Supervised Domain Adaptation

Abstract: Semi-supervised domain adaptation (SSDA) has been extensively researched due to its ability to improve classification performance and generalization ability of models by using a small amount ...
IEEE

Regularization Strategy for Point Cloud via Rigidly Mixed Sample

Abstract: Data augmentation is an effective regularization strategy to alleviate the overfitting, which is an inherent drawback of the deep neural networks. However, data augmentation is rarely ...
GitHub

3_L1_L2_Regularization_README.md

L1 and L2 Regularization are techniques used in machine learning to prevent overfitting by penalizing model complexity. They add a regularization term to the loss function, which constrains the ...
Scientific Research Publishing

Belkin, M., Niyogi, P. and Sindhwani, V. (2006) Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples. Journal of Machine Learning ...

ABSTRACT: The stochastic configuration network (SCN) is an incremental neural network with fast convergence, efficient learning and strong generalization ability, and is widely used in fields such as ...