In the field of Support Vector Machines (SVM), the traditional approach to classifier construction relies heavily 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. This thesis proposes a novel SVM methodology that gives additional weight to a curated collection of elite observations that play crucial roles in constructing SVM decision boundaries under various loss functions. These elite observations are identified through their recurring presence as support vectors in different SVM configurations using diverse loss functions. We develop new loss functions to emphasize the importance of these elite observations during the training of our SVM classifiers. The loss functions for the Elite-Driven Support Vector Machine (EDSVM) are designed to be classification-calibrated, ensuring theoretical soundness while enhancing the model's focus on these elite observations. Rigorous theoretical results are provided, and a comprehensive numerical data analysis is conducted to evaluate the EDSVM's performance across various datasets. The novel SVM models developed in this research demonstrate superior performance compared to conventional models studied in this thesis. This is evidenced through both simulation studies and real data analyses, applicable to both linear and non-linear classification tasks.