Support Vector

Support vector machines (SVMs) are powerful classification algorithms aiming to maximize the margin between data classes. Current research focuses on extending SVMs to handle large datasets and complex data structures, including integrating them with deep learning models (Deep Support Vectors) and developing efficient training methods for various applications like one-class classification and semi-supervised learning. These advancements improve the accuracy, efficiency, and interpretability of SVMs, leading to impactful applications in diverse fields such as disease prediction, agriculture, and anomaly detection. Furthermore, research explores the theoretical underpinnings of SVM generalization and convergence rates.