Title: The Counter-Intuitive Properties of Ensembles for Machine Learning, or, Democracy Speaker: Philip Kegelmeyer, Sandia National Laboratories, CA Date/Time: Thursday, September 13, 2007, 10:30 am - 11:30 am Location: CSRI Building, Room 90 (Sandia NM) Brief Abstract: Machine learning is the process of using past experience to predict the future. There are many machine learning methods; neural nets, support vector machines, decision trees. The design trade-offs in optimizing them is a tricky business, still more art than science. "Ensembles" are a machine-learning meta-method that can be applied to most machine learning algorithms. Ensembles generally greatly improve accuracy, provably do no harm, reduce or remove most of the design issues, are admirably suited to parallel and distributed computation, and are delightfully weird and counter-intuitive. This talk will provide an terse introduction to machine learning and then discuss the properties of ensembles; what they are, various theories on why they work, and how they can be simply applied to improve existing machine learning code in situ. CSRI POC: Danny Dunlavy, (505) 284-6092 |