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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 Wiley.

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Presentation on theme: "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."— Presentation transcript:

1 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

2 Chapter 2 (part 3) Bayesian Decision Theory (Sections 2-6,2-9) Discriminant Functions for the Normal Density Bayes Decision Theory – Discrete Features

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

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

5 Pattern Classification, Chapter 2 (Part 3) 4 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: g i (x) = g j (x)

6 Pattern Classification, Chapter 2 (Part 3) 5

7 6 The hyperplane separating R i and R j always orthogonal to the line linking the means!

8 Pattern Classification, Chapter 2 (Part 3) 7

9 8

10 9 Case  i =  (covariance of all classes are identical but arbitrary!) Hyperplane separating R i and R j (the hyperplane separating R i and R j is generally not orthogonal to the line between the means!)

11 Pattern Classification, Chapter 2 (Part 3) 10

12 Pattern Classification, Chapter 2 (Part 3) 11

13 Pattern Classification, Chapter 2 (Part 3) 12 Case  i = arbitrary The covariance matrices are different for each category (Hyperquadrics which are: hyperplanes, pairs of hyperplanes, hyperspheres, hyperellipsoids, hyperparaboloids, hyperhyperboloids)

14 Pattern Classification, Chapter 2 (Part 3) 13

15 Pattern Classification, Chapter 2 (Part 3) 14

16 Pattern Classification, Chapter 2 (Part 3) 15 Bayes Decision Theory – Discrete Features Components of x are binary or integer valued, x can take only one of m discrete values v 1, v 2, …, v m Case of independent binary features in 2 category problem Let x = [x 1, x 2, …, x d ] t where each x i is either 0 or 1, with probabilities: p i = P(x i = 1 |  1 ) q i = P(x i = 1 |  2 )

17 Pattern Classification, Chapter 2 (Part 3) 16 The discriminant function in this case is:

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