- Friday, September 7 at 4:00pm in Ritter 231 with refreshments beforehand in the Ritter Hall Lobby.
- Haiyan Cai, UMSL.
- Classification and Hypothesis Testing
- Abstract: Robust classification algorithms (random forests, support vector machines, deep neural networks, for example) have been developed in recent years with great success. To take advantage of this development , we recast the classical two-sample test problem in the framework of a classification problem. Based on the estimates of class probabilities from a classifier trained from the samples, we propose a new method for the two-sample test. We explain why such a test can be a powerful test and compare its performance in terms of power and efficiency with those of some other recently proposed tests with some simulation and real-life data. Our method is nonparametric and can be applied to complex and high dimensional data whenever there is a good classifier that provides uniformly consistent estimate of class probabilities for such data. The talk will start with a general introduction of the classification problem in machine learning and the basic concepts in hypothesis testing in statistics.
- TIME+DATE : 4:10pm--5:00pm Tue 04 Sep 2018
- ROOM : 216 Ritter Hall
- TITLE: Metric spaces are paracompact
- SPEAKER: Qayum Khan, SLU
- ABSTRACT: This is a learning talk. We go through Mary Ellen Rudin's clever one-page proof of Stone's theorem, which states that all metric spaces are paracompact. We will review all necessary definitions. Graduate students who have taken or are taking General Topology I (point-set topology) are encouraged to attend.