'Discriminant function' presentation slideshows

NUMERICAL ANALYSIS OF BIOLOGICAL AND ENVIRONMENTAL DATA

NUMERICAL ANALYSIS OF BIOLOGICAL AND ENVIRONMENTAL DATA. Lecture 9. Discriminant Analysis. DISCRIMINANT ANALYSIS. Discriminant analysis of two groups Assumptions of discriminant analysis - Multivariate normality Homogeneity Comparison of properties of two groups

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Motivation

Object Recognition by Discriminative Methods Sinisa Todorovic 1st Sino-USA Summer School in VLPR July, 2009. Motivation. Discriminate yes, but what?. Problem 1: What are good features. from Kristen Grauman, B. Leibe. Motivation. Discriminate yes, but against what?.

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Bruce Mayer, PE Licensed Electrical & Mechanical Engineer BMayer@ChabotCollege.edu

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Lecture 9: Discriminant function analysis (DFA)

Rationale and use of DFA The underlying model (What is a discriminant function anyway?) Finding discriminant functions: principles and procedures. Linear versus quadratic discriminant functions Significance testing Rotating discriminant functions

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From last time: PR Methods

From last time: PR Methods. Feature extraction + Pattern classification Training, testing, overfitting, overtraining Minimum distance methods Discriminant Functions Linear Nonlinear (e.g, quadratic, neural networks) -> Statistical Discriminant Functions. Statistical Pattern Recognition.

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Biometrics on the Lake IBS Australian Regional Conference 2009 Taupo, New Zealand, 29 Nov - 3 Dec

Comparison of the performance of QDF with that of the discriminant function (AEDC) based on absolute deviation from the mean. Biometrics on the Lake IBS Australian Regional Conference 2009 Taupo, New Zealand, 29 Nov - 3 Dec. S. Ganesalingam* S. Ganesh* and A. Nanthakumar #

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Discriminant Analysis

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Supervised classification

Statistical Analysis of Microarray Data. Supervised classification. Supervised classification - Introduction. Clustering consists in grouping objects without any a priori definition of the groups. The group definition emerge from the clustering itself. Clustering is thus unsupervised.

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COMPLETE BUSINESS STATISTICS

COMPLETE BUSINESS STATISTICS. by AMIR D. ACZEL & JAYAVEL SOUNDERPANDIAN 6 th edition. Chapter 17. Multivariate Analysis. The Multivariate Normal Distribution Discriminant Analysis Principal Components and Factor Analysis Using the Computer. 17. Multivariate Analysis.

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Linear Models for Classification

Linear Models for Classification. Chapter 4. Decision Theory. Inference step Use training data to learn a model for . Decision step For given x , use to make optimal class assignments. Decision Theory. Nonprobabilistic Method

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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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Generative classifiers: The Gaussian classifier

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Discrim Continued

Discrim Continued. Psy 524 Andrew Ainsworth. Types of Discriminant Function Analysis. They are the same as the types of multiple regression

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Use of Frequency Domain to Determine Border of Subthalamic Nucleus: Proof of Concept

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LECTURE 32: DISCRIMINATIVE TRAINING

LECTURE 32: DISCRIMINATIVE TRAINING. Objectives: Bayes Rule Mutual Information Conditional Likelihood Mutual Information Estimation (CMLE) Maximum MI Estimation (MMIE) Minimum Classification Error (MCE)

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LECTURE 31: DISCRIMINATIVE TRAINING

LECTURE 31: DISCRIMINATIVE TRAINING. Objectives: Bayes Rule Mutual Information Conditional Likelihood Mutual Information Estimation (CMLE) Maximum MI Estimation (MMIE) Minimum Classification Error (MCE)

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Discriminant Function Analysis

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Identifiction of IBD using an electronic e-nose

Identifiction of IBD using an electronic e-nose. Gastroenterology: Arasaradnam RP 1,2 - r.arasaradnam@warwick.ac.uk Engineering: Covington JA 3, - j.a.covington@warwick.ac.uk 1University Hospital Coventry & Warwickshire & 2Clinical Sciences Research Institute, Medical School,

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Multiple-group linear discriminant function

Multiple-group linear discriminant function. maximum & contributing ldf dimensions concentrated & diffuse ldf structures. Like ANOVA, ldf can be applied to more than two groups.

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