Vector Machine

Vector machines are a class of supervised machine learning algorithms used for classification and regression tasks, aiming to find an optimal hyperplane that maximizes the margin between different data classes. Current research focuses on improving their efficiency and accuracy through techniques like curriculum learning to optimize training sample order, Bayesian formulations to mitigate overfitting in high-dimensional datasets ("fat data"), and the exploration of novel kernel functions such as generalized Gaussian RBFs. These advancements are impacting diverse fields, including medical diagnosis (e.g., Alzheimer's disease and Moyamoya disease prediction), financial forecasting (e.g., petroleum price prediction), and personalized education, demonstrating their broad applicability and practical significance.