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.

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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.

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.

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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.

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.

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.

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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.

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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.

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Presentation transcript:

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) Discriminant Functions for the Normal Density Bayes Decision Theory – Discrete Features

Discriminant Functions for the Normal Density We saw that the minimum error-rate classification can be achieved by the discriminant function gi(x) = ln P(x | i) + ln P(i) Case of multivariate normal Pattern Classification, Chapter 2 (Part 3)

Case i = 2.I (I stands for the identity matrix) Pattern Classification, Chapter 2 (Part 3)

A classifier that uses linear discriminant functions is called “a linear machine” The decision surfaces for a linear machine are pieces of hyperplanes defined by: gi(x) = gj(x) Pattern Classification, Chapter 2 (Part 3)

Pattern Classification, Chapter 2 (Part 3)

always orthogonal to the line linking the means! The hyperplane separating Ri and Rj always orthogonal to the line linking the means! Pattern Classification, Chapter 2 (Part 3)

Pattern Classification, Chapter 2 (Part 3)

Pattern Classification, Chapter 2 (Part 3)

Case i =  (covariance of all classes are identical but arbitrary!) Hyperplane separating Ri and Rj (the hyperplane separating Ri and Rj is generally not orthogonal to the line between the means!) Pattern Classification, Chapter 2 (Part 3)

Pattern Classification, Chapter 2 (Part 3)

Pattern Classification, Chapter 2 (Part 3)

Case i = arbitrary The covariance matrices are different for each category (Hyperquadrics which are: hyperplanes, pairs of hyperplanes, hyperspheres, hyperellipsoids, hyperparaboloids, hyperhyperboloids) Pattern Classification, Chapter 2 (Part 3)

Pattern Classification, Chapter 2 (Part 3)

Pattern Classification, Chapter 2 (Part 3)

Bayes Decision Theory – Discrete Features Components of x are binary or integer valued, x can take only one of m discrete values v1, v2, …, vm Case of independent binary features in 2 category problem Let x = [x1, x2, …, xd ]t where each xi is either 0 or 1, with probabilities: pi = P(xi = 1 | 1) qi = P(xi = 1 | 2) Pattern Classification, Chapter 2 (Part 3)

The discriminant function in this case is: Pattern Classification, Chapter 2 (Part 3)