PPT-Robust Linear Registration of CT images using Random Regression Forests

Ender Konukoglu 1 Antonio Criminisi 1 Sayan Pathak 2 Duncan Robertson 1 Steve White 2 David Haynor 3 and Khan Siddiqui 2 1 Microsoft Research Cambridge 2 Microsoft Corporation . One remedy is to remove in57567uential observations from the leastsquares 64257t see Chapter 6 Section 61 in the text Another approach termed robust regression istoemploya64257tting criterion that is not as vulnerable as least squares to unusual dat a highly accurate and interpretable ensemble predictor. Song . L, . Langfelder. P, Horvath S. . BMC . Bioinformatics . 2013. Steve Horvath (. shorvath@mednet.ucla.edu. ) . University of California, Los . Instructional Materials. http://. core.ecu.edu/psyc/wuenschk/PP/PP-MultReg.htm. aka. , . http://tinyurl.com/multreg4u. Introducing the General. Linear Models. As noted by the General, the GLM can be used to relate one set of things (. Nonrigid. Registration by Convex Optimization. Qifeng. Chen. Stanford University. Vladlen. . Koltun. Intel Labs. Nonrigid. Registration . Intra-subject registration. Nonrigid. Registration. Inter-subject registration. Linear Function. Y = a + bX. Fixed and Random Variables. A FIXED variable is one for which you have every possible value of interest in your sample.. Example: Subject sex, female or male.. A RANDOM variable is one where the sample values are randomly obtained from the population of values.. William Greene. Department of Economics. Stern School of Business. Descriptive . Statistics and Linear Regression. Model Building in Econometrics. Parameterizing the model. Nonparametric analysis. Semiparametric analysis. Lectures 1-2. David Woodruff. IBM Almaden. Massive data sets. Examples. Internet traffic logs. Financial data. etc.. Algorithms. Want nearly linear time or less . Usually at the cost of a randomized approximation. ;. some. do’s . and. . don’ts. Hans Burgerhof. Medical. . S. tatistics. and . Decision. Making. Department. of . Epidemiology. UMCG. . Help! Statistics! Lunchtime Lectures. When?. Where?. What?. David J Corliss, PhD. Wayne State University. Physics and Astronomy / Public Outreach. Model Selection Flowchart. NON-LINEAR. LINEAR MIXED. NON-PARAMETRIC. Decision: Continuous or Discrete Outcome. PROC LOGISTIC. 1. Correlation indicates the magnitude and direction of the linear relationship between two variables. . Linear Regression: variable Y . (criterion) . is predicted by variable X . (predictor) . using a linear equation.. What. is . what. ? . Regression: One variable is considered dependent on the other(s). Correlation: No variables are considered dependent on the other(s). Multiple regression: More than one independent variable. Nisheeth. Linear regression is like fitting a line or (hyper)plane to a set of points. The line/plane must also predict outputs the unseen (test) inputs well. . Linear Regression: Pictorially. 2. (Feature 1). 1. 2. Office Hours. :. More office hours, schedule will be posted soon.. . On-line office hours are for everyone, please take advantage of them.. . Projects:. Project guidelines and project descriptions will be posted Thursday 9/25.. Regression Trees. Characteristics of classification models. model. linear. parametric. global. stable. decision tree. no. no. no. no. logistic regression. yes. yes. yes. yes. discriminant. analysis.