AI RESEARCH
Elite-Driven Support Vector Machines for Classification
arXiv CS.LG
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ArXi:2604.25158v1 Announce Type: cross vector machines (SVMs) are a standard tool for binary classification, but their classical formulations are purely data-driven and offer no direct way to encode trusted benchmark models or structured preferences on selected subsets of the data. We propose Elite-Driven Vector Machines (EDSVM), a general framework that augments regularized empirical risk minimization by guiding the slack variables for a curated set of elite observations (typically the union of vectors from one or reference SVMs.