'Discriminant functions' presentation slideshows

Review

Review. Alexandros Potamianos Dept of ECE, Tech. Univ. of Crete Fall 2004-2005. PatReco: Introduction. Alexandros Potamianos Dept of ECE, Tech. Univ. of Crete Fall 2004-2005. PatReco:Applications. Speech/audio/music/sounds Speech recognition, Speaker verification/id, Image/video

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856 views • 55 slides

Linear Methods For Classification Chapter 4

693 views • 50 slides

Speech Recognition

Speech Recognition. Pattern Classification. Pattern Classification. Introduction Parametric classifiers Semi-parametric classifiers Dimensionality reduction Significance testing. Classifier. Feature Extraction. Class ? i. Feature Vectors x. Observation s. Pattern Classification.

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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

669 views • 41 slides

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!!!

682 views • 41 slides

Speech Recognition

Speech Recognition. Pattern Classification. Pattern Classification. Introduction Parametric classifiers Semi-parametric classifiers Dimensionality reduction Significance testing. Classifier. Feature Extraction. Class ? i. Feature Vectors x. Observation s. Pattern Classification.

563 views • 40 slides

Support Vector Machines and Kernel Methods

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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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Summarized and revised by Hee-Woong Lim

Ch 4. Linear Models for Classification (1/2) Pattern Recognition and Machine Learning, C. M. Bishop, 2006. Summarized and revised by Hee-Woong Lim. Contents. 4.1. Discriminant Functions 4.2. Probabilistic Generative Models. Classification Models. Linear classification model

408 views • 30 slides

METU Informatics Institute Min720 Pattern Classification with Bio-Medical Applications

METU Informatics Institute Min720 Pattern Classification with Bio-Medical Applications. Part 7 : Linear and G eneralized D iscriminant F unctions. LINEAR DISCRIMINANT FUNCTIONS. Assume that t he discriminant functions are linear function s of X.

411 views • 29 slides

Spatial Statistics

Spatial Statistics. Point Patterns. Spatial Statistics. Increasing sophistication of GIS allows archaeologists to apply a variety of spatial statistics to their data Predictive Modeling Intra-site Spatial Analysis. Predictive Modeling 1. Goal is to predict where sites will be located

566 views • 27 slides

Linear Models for Classification

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Linear Models for Classification

1.07k views • 25 slides

Presentation agenda

Neural Network Ensemble based on Feature Selection for Non-Invasive Recognition of Liver Fibrosis Stage. Bartosz KRAWCZYK, Micha? WO?NIAK, Tomasz ORCZYK, Piotr PORWIK, Joanna MUSIALIK, Barbara B?O?SKA-FAJFROWSKA. Presentation agenda. Overview. Current diagnostic methods. Proposed method.

292 views • 18 slides

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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Linear Discriminant Analysis

Linear Discriminant Analysis. Linear Discriminant Analysis. Why To identify variables into one of two or more mutually exclusive and exhaustive categories. To examine whether significant differences exist among the groups in terms of the predictor variables. What

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Linear Discriminant Analysis (LDA)

Linear Discriminant Analysis (LDA). Goal . To classify observations into 2 or more groups based on k discriminant functions (Dependent variable Y is categorical with k classes.) Assumptions Multivariate Normal Distribution variables are distributed normally within the classes/groups.

1.76k views • 10 slides

A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions

A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions. Zhihong Zeng, Maja Pantic, Glenn I. Roisman, Thomas S. Huang. Reported by Chengsheng Mao 2011 ? 1 ? 11 ?. The Description of Emotion.

186 views • 9 slides