In SVM, the construction of classifiers relies on a set of observations known as support vectors, determined by the choice of loss functions. Each loss function results in a specific decision boundary and identifies a unique set of support vectors, leading to varied classification performances. We propose a novel SVM methodology that gives additional weight to a collection of elite observations that play key roles in constructing SVM decision boundaries under various loss functions. These elite observations are identified as support vectors in various SVM models. We construct new loss functions to highlight the role of these elite observations during the training of our SVM classifiers. The loss functions for EDSVM are designed to be classification-calibrated, ensuring that they theoretically sound while enhancing the model's focus on these elite observations. Theoretical findings are presented, alongside thorough numerical analysis, to assess EDSVM's efficacy across various datasets.