From Wikipedia, the free encyclopedia Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. Statistical learning theory deals with the problem of finding a predictive function based on data. Statistical learning theory has led to successful applications in fields such as computer vision, speech recognition, and bioinformatics. It is the theoretical framework underlying support vector machines.
Faculty in this Research Group
Daniel McDonald, Assistant Professor
Education: Ph.D., Statistics, Carnegie Mellon University, 2012