Support Vector Machine Extensions: SVM, RVM, Transductive SVM

This lecture covers advanced topics in machine learning, focusing on Support Vector Machine (SVM) extensions such as Relevance Vector Machine (RVM), Transductive SVM, and Support Vector Clustering. It discusses the concepts of clustering, semi-supervised clustering, and classification, emphasizing the use of labels to determine class boundaries. The lecture explores the decision function of SVM, different types of boundaries generated by polynomial kernels, and the role of parameters like kernel width and number of clusters. It also delves into the characteristics and optimization of RVM, the differences between SVM and RVM, and the application of SVM in transductive clustering. The exercises include solving SVM problems, understanding error bounds, and exploring the support vectors' influence on classification.