Random Vector Functional Link

Random Vector Functional Link (RVFL) networks are single-hidden-layer neural networks employing randomized weights, enabling fast training and avoiding the complexities of iterative optimization. Current research focuses on enhancing RVFL's capabilities through various modifications, including incorporating granular computing, multi-view learning, and novel loss functions like wave loss, as well as exploring ensemble and deep RVFL architectures. These advancements aim to improve RVFL's robustness to noise, scalability to large datasets, and overall predictive accuracy across diverse applications, such as protein prediction and image classification. The resulting improvements in efficiency and performance make RVFL a valuable tool for various machine learning tasks.