In the previous post on Support Vector Machines (SVM), we looked at the mathematical details of the algorithm. In this post, I will be discussing the practical implementations of SVM for classification as well as regression. I will be using the iris dataset as an example for the classification problem, and a randomly generated data as an example for the regression problem.
Last week I had the privilege of invited to be the opening keynote at the University of Plymouth's annual learning and teaching event. The first day of the the two day celebration of learning and teaching was devoted to digital learning developments. The day had a strong focus on learning analytics and digital capabilities.
J. Tang, H. fung Leung, Q. Luo, D. Chen, and J. Gong. IJCAI'09: Proceedings of the 21st international jont conference on Artifical intelligence, page 2089--2094. San Francisco, CA, USA, Morgan Kaufmann Publishers Inc., (2009)