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Webinar: Mohammad Jafari Jozani

July 1 @ 14:00 - 15:00

On-site and online seminar: Tuesday, July 1st 2025 from 2.00 – 3.00 PM

Title: Rethinking Support in SVMs: An Elite-Driven Approach to Classification

Speaker: Mohammad Jafari Jozani (University of Manitoba)

Location: Aula 205 (ex 32) – Viale Morgagni 59

Please register here to participate online : link

ABSTRACT

Support Vector Machines (SVMs) are a cornerstone of classification methodology, where decision boundaries are shaped by support vectors determined via a chosen loss function. However, different loss functions yield different sets of support vectors, resulting in variable classification outcomes. This conventional dependence on a single loss function often obscures broader structural insights—particularly the presence of observations that consistently act as support vectors across multiple SVM configurations. These persistently influential points point to a new paradigm in classifier design.

We propose Elite-Driven Support Vector Machines (EDSVM), a novel framework that enhances classification performance by identifying and amplifying the role of these elite observations. These elites are data points that recur as support vectors across a range of loss functions and decision boundaries. To harness their importance, we design new classification-calibrated loss functions that embed elite-weighted influence directly into the training process.

Through rigorous theoretical development and extensive empirical evaluation—spanning both synthetic and real-world datasets—we show that EDSVM outperforms classical SVMs in linear and nonlinear settings. This work advances the foundations of SVM methodology and offers practical tools for high-stakes, data-sensitive applications.

Details

Date:
July 1
Time:
14:00 - 15:00