'Decision surface' diaporamas de présentation

Chapter 5: Linear Discriminant Functions (Sections 5.1-5.3, 5.4, 5.11)

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 5: Linear Discriminant Functions (Sections 5.1-5.3, 5.4, 5.11).

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Line ar machines 28/02/2017

Line ar machines 28/02/2017. Decision surface for Bayes classifier with Normal densites. Decison surfaces. We focus on the decision surfaces Linear machines = linear decision surface Non-optimal solution but tractable model. Decision tree and decision regions. Line ar discriminant function.

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Linear classifiers

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Pattern recognition

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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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Non-Bayes classifiers.

Non-Bayes classifiers. Linear discriminants, neural networks. Discriminant functions(1). Bayes classification rule:. Instead might try to find a function:. is called discriminant function. - decision surface. Class 1. Class 1. Class 2. Class 2. Discriminant functions (2).

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Linear Discriminant Functions

Linear Discriminant Functions. Wen-Hung Liao, 11/25/2008. Introduction: LDF. Assume we know the proper form of the discriminant functions , instead of the underlying probability densities. Use samples to estimate the parameters of the classifier.(statistical or non-statistical)

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Machine Learning

Machine Learning. Lecture 2 Perceptron. Perceptron - Basic. Perceptron is a type of artificial neural network (ANN). Perceptron - Operation.

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K-Nearest Neighbor Learning

K-Nearest Neighbor Learning. Dipanjan Chakraborty. Different Learning Methods. Eager Learning Explicit description of target function on the whole training set Instance-based Learning Learning=storing all training instances Classification=assigning target function to a new instance

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