(2012). L2 Regularization for Learning Kernels. Retrieved from http://arxiv.org/abs/1205.2653C. Cortes, M. Mohri, and A. Rostamizadeh, "L2 regularization for learning kernels," in UAI, 2009.C. Cortes, M. Mohri, and A. Rostamizadeh, "L2 regularization for learning kernels," CoRR, vol...
As a powerful tool in machine learning, support vector machine (SVM) suffers from expensive computational cost in the training phase due to the large number of original training samples. In addition, Minimal Enclosing Ball (MEB) has a limitation with a large dataset, and the training computationa...
After evaluating the performance of different deep learning models, the optimized Early Stopping—L2 Norm Regularization—Visual Geometry Group 16 (ES-L2-VGG16) model is developed to enhance accuracy and efficiency in identifying IA hidden dangers. The core purpose is to explore the application of ...
Laplacian Welsch Regularization for Robust Semisupervised Learning. IEEE Trans. Cybern. 2020, 52, 164–177. [Google Scholar] [CrossRef] [PubMed] Tokgoz, E.; Trafalis, T.B. Mixed convexity & optimization of the SVM QP problem for nonlinear polynomial kernel maps. In Proceedings of the 5th...