Lecture
This lecture covers the concept of Transductive Support Vector Machine, which learns from partially labeled datapoints, aiming for zero error on labeled points and well-separated unlabeled points. It explores the mix between a classification and clustering problem, using labels to guide the separation. The lecture also delves into the constraints and optimization problems involved in Transductive SVM.