AI RESEARCH
Truncated Kernel Stochastic Gradient Descent with General Losses and Spherical Radial Basis Functions
arXiv CS.LG
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ArXi:2510.04237v5 Announce Type: replace In this paper, we propose a novel kernel stochastic gradient descent (SGD) algorithm for large-scale supervised learning with general losses. Compared to traditional kernel SGD, our algorithm improves efficiency and scalability through an innovative regularization strategy.