'Minimum error rate classification' presentation slideshows

LECTURE 03: GAUSSIAN CLASSIFIERS

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LECTURE 03: GAUSSIAN CLASSIFIERS

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Lecture 2. Bayesian Decision Theory

Lecture 2. Bayesian Decision Theory. Multivariate normal distribution Discriminant function for normal distributions Discriminant function for discrete distribution. Normal density. Reminder: the covariance matrix is symmetric and positive semidefinite. Entropy - the measure of uncertainty

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Chapter 2 (part 3) Bayesian Decision Theory (Sections 2-6,2-9)

Pattern Classification All materials in these slides were taken from Pattern Classification (2nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John Wiley & Sons, 2000 with the permission of the authors and the publisher. Chapter 2 (part 3) Bayesian Decision Theory (Sections 2-6,2-9).

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Objectives: Linear Discriminant Functions Gradient Descent Nonseparable Data

LECTURE 20 : LINEAR DISCRIMINANT FUNCTIONS. Objectives: Linear Discriminant Functions Gradient Descent Nonseparable Data Resources: SM: Gradient Descent JD: Optimization Wiki: Stochastic Gradient Descent MJ: Linear Programming. Discriminant Functions.

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