Elite-Driven Support Vector Machines for Classification
Support 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 Support 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 support vectors from one or more reference SVMs). EDSVM combines the usual slack loss with a deviation penalty that shrinks new slacks toward benchmark slack values, defining a localized, margin-aligned notion of proxim...
| Authors | Mohammad Jafari Jozani, Bahram Moeinianfar |
|---|---|
| Primary category | stat.ML |
| Published | 28 April 2026 |
| Last updated at source | 28 April 2026 |
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