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B ayes decision theory PowerPoint Presentation
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B ayes decision theory

B ayes decision theory

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B ayes decision theory

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  1. Pattern ClassificationAll 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, 2000with the permission of the authors and the publisher

  2. Bayes decision theory febr. 17.

  3. Supervisedlearning: Basedontrainingexamples (E), learn a modell whichworksfineonpreviouslyunseenexamples. Classification: a supervisedlearningtask of categorisationofentitiesintopredefinedsetofclasses 2 Classification

  4. Pattern Classification, Chapter 2 (Part 1)

  5. Posterior, likelihood, evidence P(j | x) = P(x | j) . P (j) / P(x) Posterior = (Likelihood. Prior) / Evidence Where in case of two categories Pattern Classification, Chapter 2 (Part 1)

  6. Pattern Classification, Chapter 2 (Part 1)

  7. Bayesian Decision • Decision given the posterior probabilities X is an observation for which: if P(1 | x) > P(2 | x) True state of nature = 1 if P(1 | x) < P(2 | x) True state of nature = 2 This rule minimizes the probability of the error. Pattern Classification, Chapter 2 (Part 1)

  8. Bayesian Decision Theory – Generalization • Use of more than one feature • Use more than two states of nature • Allowing actions and not only decide on the state of nature • Introduce a loss of function which is more general than the probability of error Pattern Classification, Chapter 2 (Part 1)

  9. Let {1, 2,…, c} be the set of c states of nature (or “categories”) Let {1, 2,…, a}be the set of possible actions Let (i | j)be the loss incurred for taking action i when the state of nature is j Pattern Classification, Chapter 2 (Part 1)

  10. Bayes decision theory -example Automatictrading (onstockexchanges) 1: thepriceswillincrease (inthefuture!) 2: thepriceswill be lower 3: thepriceswon’t changetoomuch Wecannotobserve (latent)! 1:buy 2: sell x: actualprices (and historicalprices) x is observed : howmuchtolosewith an action

  11. Overall risk R = Sum of all R(i | x) for i = 1,…,a Minimizing R Minimizing R(i| x) for i = 1,…, a for i = 1,…,a Conditional risk Pattern Classification, Chapter 2 (Part 1)

  12. Select the action i for which R(i | x) is minimum R is minimum and R in this case is called the Bayes risk = best performance that can be achieved! Pattern Classification, Chapter 2 (Part 1)

  13. Two-category classification 1: deciding 1 2: deciding 2 ij = (i|j) loss incurred for deciding iwhen the true state of nature is j Conditional risk: R(1 | x) = 11P(1 | x) + 12P(2 | x) R(2 | x) = 21P(1 | x) + 22P(2 | x) Pattern Classification, Chapter 2 (Part 1)

  14. Our rule is the following: if R(1 | x) < R(2 | x) action 1: “decide 1” is taken This results in the equivalent rule : decide 1if: (21- 11) P(x | 1) P(1) > (12- 22) P(x | 2) P(2) and decide2 otherwise Pattern Classification, Chapter 2 (Part 1)

  15. Likelihood ratio: The preceding rule is equivalent to the following rule: Then take action 1 (decide 1) Otherwise take action 2 (decide 2) Pattern Classification, Chapter 2 (Part 1)

  16. Exercise Select the optimal decision where: • = {1, 2} P(x | 1) N(2, 0.5) (Normal distribution) P(x | 2) N(1.5, 0.2) P(1) = 2/3 P(2) = 1/3 Pattern Classification, Chapter 2 (Part 1)

  17. Zero-one loss function(Bayes Classifier) Therefore, the conditional risk is: “The risk corresponding to this loss function is the average probability error”  Pattern Classification, Chapter 2 (Part 2)

  18. Classifiers, Discriminant Functionsand Decision Surfaces • The multi-category case • Set of discriminant functions gi(x), i = 1,…, c • The classifier assigns a feature vector x to class i if: gi(x) > gj(x) j  i Pattern Classification, Chapter 2 (Part 2)

  19. Let gi(x) = - R(i | x) (max. discriminant corresponds to min. risk!) • For the minimum error rate, we take gi(x) = P(i | x) (max. discrimination corresponds to max. posterior!) gi(x)  P(x | i) P(i) gi(x) = ln P(x | i) + ln P(i) (ln: natural logarithm!) Pattern Classification, Chapter 2 (Part 2)

  20. Feature space divided into c decision regions if gi(x) > gj(x) j  i then x is in Ri (Rimeans assign x to i) • The two-category case • A classifier is a “dichotomizer” that has two discriminant functions g1 and g2 Let g(x)  g1(x) – g2(x) Decide 1 if g(x) > 0 ; Otherwise decide 2 Pattern Classification, Chapter 2 (Part 2)

  21. The computation of g(x) Pattern Classification, Chapter 2 (Part 2)

  22. Discriminant functions of the Bayes Classifierwith Normal Density Pattern Classification, Chapter 2 (Part 1)

  23. The Normal Density • Univariate density • Density which is analytically tractable • Continuous density • A lot of processes are asymptotically Gaussian • Handwritten characters, speech sounds are ideal or prototype corrupted by random process (central limit theorem) Where:  = mean (or expected value) of x 2 = expected squared deviation or variance Pattern Classification, Chapter 2 (Part 2)

  24. Pattern Classification, Chapter 2 (Part 2)

  25. Multivariate density • Multivariate normal density in d dimensions is: where: x = (x1, x2, …, xd)t(t stands for the transpose vector form)  = (1, 2, …, d)t mean vector  = d*d covariance matrix || and -1 are determinant and inverse respectively Pattern Classification, Chapter 2 (Part 2)

  26. 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)

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

  28. 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)

  29. The hyperplane is always orthogonal to the line linking the means! Pattern Classification, Chapter 2 (Part 3)

  30. The hyperplane separatingRiand Rj always orthogonal to the line linking the means! Pattern Classification, Chapter 2 (Part 3)

  31. Pattern Classification, Chapter 2 (Part 3)

  32. Pattern Classification, Chapter 2 (Part 3)

  33. 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)

  34. Pattern Classification, Chapter 2 (Part 3)

  35. Pattern Classification, Chapter 2 (Part 3)

  36. 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)

  37. Pattern Classification, Chapter 2 (Part 3)

  38. Pattern Classification, Chapter 2 (Part 3)

  39. Exercise Select the optimal decision where: • = {1, 2} P(x | 1) N(2, 0.5) (Normal distribution) P(x | 2) N(1.5, 0.2) P(1) = 2/3 P(2) = 1/3 Pattern Classification, Chapter 2

  40. Parameter estimation Pattern Classification, Chapter 3

  41. Data availability in a Bayesian framework • We could design an optimal classifier if we knew: • P(i) (priors) • P(x | i) (class-conditional densities) Unfortunately, we rarely have this complete information! • Design a classifier from a training sample • No problem with prior estimation • Samples are often too small for class-conditional estimation (large dimension of feature space!) 1

  42. A priori information about the problem • E.g. assume normality of P(x | i) P(x | i) ~ N( i, i) Characterized by 2 parameters • Estimation techniques • Maximum-Likelihood (ML) and the Bayesian estimations • Results are nearly identical, but the approaches are different 1

  43. Parameters in ML estimation are fixed but unknown! • Best parameters are obtained by maximizing the probability of obtaining the samples observed • Bayesian methods view the parameters as random variables having some known distribution • In either approach, we use P(i | x)for our classification rule! 1

  44. Use the informationprovided by the training samples to estimate  = (1, 2, …, c), each i (i = 1, 2, …, c) is associated with each category • Suppose that D contains n samples, x1, x2,…, xn • ML estimate of  is, by definition the value that maximizes P(D | ) “It is the value of  that best agrees with the actually observed training sample” 2

  45. Optimal estimation • Let  = (1, 2, …, p)t and let  be the gradient operator • We define l() as the log-likelihood function l() = ln P(D | ) • New problem statement: determine  that maximizes the log-likelihood 2

  46. Bayesian Estimation • In MLE  was supposed fix • In BE  is a random variable • The computation of posterior probabilities P(i | x) lies at the heart of Bayesian classification • Goal: compute P(i | x, D) Given the sample D, Bayes formula can be written

  47. Pattern Classification, Chapter 1

  48. Bayesian Parameter Estimation: Gaussian Case Goal: Estimate  using the a-posteriori density P( | D) • The univariate case: P( | D)  is the only unknown parameter (0 and 0 are known!)

  49. Reproducing density Identifying (1) and (2) yields:

  50. The univariate case P(x | D) • P( | D) computed • P(x | D) remains to be computed! It provides: (Desired class-conditional density P(x | Dj, j)) Therefore: P(x | Dj, j) together with P(j) and using Bayes formula, we obtain theBayesian classification rule: