PPT-Bayesian Nonparametric Classification and Applications

Department of Electrical and Computer Engineering Zhu Han Department of Electrical and Computer Engineering University of Houston Thanks to Nam Nguyen Guanbo Zheng and Dr Rong . We propose a nonparametric di64256eomorphic image registra tion algorithm based on Thirions demons algorithm The dem ons algo rithm can be seen as an optimization procedure on the entire s pace of displacement 64257elds The main idea of our algorith isavectorofparameterstobeestimatedand x isavectorofpredictors forthe thof observationstheerrors areassumedtobenormallyandindependentlydistributedwith mean 0 and constant variance The function relating the average value of the response to the pred De64257nition A Bayesian nonparametric model is a Bayesian model on an in64257nitedimensional parameter space The parameter space is typically chosen as the set of all possi ble solutions for a given learning problem For example in a regression prob Week 9 and Week 10. 1. Announcement. Midterm II. 4/15. Scope. Data . warehousing and data cube. Neural . network. Open book. Project progress report. 4/22. 2. Team Homework Assignment #11. Read pp. 311 – 314.. Nonparametrics. via Probabilistic . Programming . Frank Wood. fwood@robots.ox.ac.uk. http://. www.robots.ox.ac.uk. /~. fwood. MLSS 2014. May, . 2014 Reykjavik. Excellent tutorial dedicated to Bayesian . Naïve . Bayes. 2. What happens if we have more than one piece of evidence?. If we can assume conditional independence. Overslept . and . trafficjam. . are independent, given . late. A and B are conditionally independent given C just in case B doesn't tell us anything about A if we already know C:. Misstear. Spam Filtering. An Artificial Intelligence Showcase. What is Spam. Messages sent indiscriminately to a large number of recipients. We all hate it. Term attributed to a Monty Python skit. Legitimate messages sometimes referred to as “ham. Alex Yakubovich. Elderlab. Oct 7, 2011. John Wilder, Jacob Feldman, Manish Singh, . Superordinate shape classification using natural shape statistics. , Cognition, Volume 119, Issue 3, June 2011, Pages 325-340. TO. . Machine . Learning. 3rd Edition. ETHEM . ALPAYDIN. © The MIT Press, . 2014. alpaydin@boun.edu.tr. http://www.cmpe.boun.edu.tr/~. ethem/i2ml3e. Lecture Slides for. CHAPTER . 16:. . Bayesian Estimation. Asymptotics. Yining Wang. , Jun . zhu. Carnegie Mellon University. Tsinghua University. 1. Subspace Clustering. 2. Subspace Clustering Applications. Motion Trajectories tracking. 1. 1 . (. Elhamifar. . Regression. COSC 878 Doctoral Seminar. Georgetown University. Presenters:. . Sicong Zhang. , . Jiyun. . Luo. .. April. . 1. 4. , 201. 5. 5.0. . Nonparametric Regression. 2. 5.0. . Nonparametric Regression. Making Decisions Under uncertainty. 1. Overview. Basics of Probability and the Bayes Rule. Bayesian . Classification. Losses and . Risks. Discriminant Function. Utility Theory. Association . Rule Learning. Machine Learning. Chapter 2: Probability distributions. Parametric Distributions. Basic building blocks:. Need to determine given . Representation: or ?. Recall Curve Fitting. . conditional . VaR. . and . expected shortfall. Outline. Introduction. Nonparametric . Estimators. Statistical . Properties. Application. Introduction. Value-at-risk (. VaR. ) and expected shortfall (ES) are two popular measures of market risk associated with an asset or portfolio of assets..