Sparse Learning and Class Probability Estimation with Weighted Support Vector Machines

ArXi:2312.10618v2 Announce Type: replace-cross Classification and probability estimation are fundamental tasks with broad applications across modern machine learning and data science, spanning fields such as biology, medicine, engineering, and computer science. Recent development of weighted Vector Machines (wSVMs) has nstrated considerable promise in robustly and accurately predicting class probabilities and performing classification across a variety of problems (Wang, 2008