Support Vector Machine

Support Vector Machines (SVMs) are powerful machine learning algorithms aiming to find optimal hyperplanes that maximize the margin between different data classes. Current research focuses on improving SVM efficiency and robustness, particularly for large datasets and noisy data, through advancements in model architectures like Twin SVMs and the exploration of novel loss functions (e.g., guardian loss, wave loss, p-norm hinge loss). These improvements enhance SVM applicability across diverse fields, including medical diagnosis, image classification, and fault detection, by increasing accuracy and scalability while mitigating overfitting and sensitivity to outliers.