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Kernel Discriminant Analysis using Bayesian Model Averaging

Dec 12, 2012

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Outline. What is Discriminant Analysis?Kernel Discriminant AnalysisSelection of Bandwidth-Bayesian Model Averaging using MCMCSupervised LearningSemi-Supervised LearningComputational Issues Analysis of Benchmark Data sets. 2 Fold Problem. Our Contributions. An Example. A credit card company

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Kernel Discriminant Analysis using Bayesian Model Averaging

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Discriminant Analysis

Discriminant Analysis . Similar to Regression, except that criterion (or dependent variable) is categorical rather than continuous. -used to identify boundaries between groups of objects. For example: (a) does a person have the disease or not (b) Is someone a good credit risk or not?

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Discriminant Analysis

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Probabilistic Weather Forecasting Using Bayesian Model Averaging. J. McLean Sloughter Adviser: Tilmann Gneiting GSR: Susan Joslyn Committee members: Adrian Raftery &amp; Cliff Mass 8 May, 2009. This work was supported by MURI, JEFS, &amp; NSF grants. Background Motivation Ensemble forecasting

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Discriminant Analysis

Discriminant Analysis. DA is used for discriminating among groups by analyzing data with a discrete or categorical dependent variable and a set of independent variables.

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Discriminant Analysis

Chapter XVIII. Discriminant Analysis. Chapter Outline 1) Overview 2) Basic Concept 3) Relation to Regression and ANOVA 4) Discriminant Analysis Model 5) Statistics Associated with Discriminant Analysis 6) Conducting Discriminant Analysis i. Formulation ii. Estimation

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Chapter 18. Discriminant Analysis. Content. Fisher discriminant analysis Maximum likelihood method Bayes formula discriminant analysis Bayes discriminant analysis Stepwise discriminant analysis.

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Discriminant Analysis. Susan Kolenko Feb. 26, 2009 STAT 8320. Review Objectives of Discriminant Analysis Example using SAS. Tonight’s Program:. Introduction and Review. Discriminant Analysis is appropriate for what type of Response variables? NOMINAL CATEGORICAL

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Discriminant Analysis

Discriminant Analysis. An Introduction. Problem description. We wish to predict group membership for a number of subjects from a set of predictor variables. The criterion variable (also called grouping variable) is the object of classification. This is ALWAYS a categorical variable!!!

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