Kernel Machine

Kernel machines are a class of powerful machine learning models that leverage kernel functions to perform nonlinear data analysis, primarily aiming for accurate prediction and efficient computation. Current research focuses on improving scalability through techniques like Nystrom approximation and tensor network constraints, exploring novel architectures such as recursive feature machines and deep restricted kernel machines, and integrating prior knowledge via logic constraints or multi-view learning. These advancements enhance the applicability of kernel methods to large datasets and complex tasks, impacting fields ranging from biomedical classification to quantum chemistry through improved accuracy, efficiency, and interpretability.