Statistical Pattern Recognition and Genome Data Analysis

Abstract

This talk gives a brief overview of statistical pattern recognition in recent developments including several methods of boost learning algorithms. I focus on a class of the expected and empirical loss functions with Bayes risk consistency. Probabilistic discussion is given to the class. Next as a special application I introduce statistical analyses for gene and protein expression data sets to extract significant patterns associated with diseases or treatment effects in medical contexts.