A Sparse, Flexible Framework with Confidence Measure Based on the Relevance Vector Machine

Watch a 48-minute research seminar where Dr. Anita Faul presents a mathematical framework based on the Relevance Vector Machine, focusing on sparse and flexible approaches with confidence measures. Delivered as part of the Discretization and Recovery in High-dimensional Spaces (DRE) programme at the Isaac Newton Institute, explore cutting-edge developments in mathematical sciences and their applications across science and technology fields. Gain insights into this specialized topic through the presentation given at the prestigious research institute that brings together leading mathematical scientists for extended research collaborations.