Lecture
This lecture covers the Relevance Vector Machine (RVM) as a solution to the shortcomings of the Support Vector Machine (SVM). RVM aims to provide a sparse solution by rewriting the SVM solution in a linear form. It introduces a prior distribution on the parameters to prevent overfitting and uses a Bayesian approach to estimate the model likelihood. The iterative procedure for computing optimal parameters is discussed, along with the importance of zero-mean prior distribution. The lecture emphasizes the need to handle false positives carefully and highlights the uncertainty encapsulated in the model's distribution.