1 / 25

Pattern Recognition: Statistical and Neural

This lecture discusses the classifier framework and performance measures in pattern recognition, including the a' posteriori probability, probability of error, Bayes average cost, probability of detection, likelihood ratio, and more.

eddins
Télécharger la présentation

Pattern Recognition: Statistical and Neural

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Nanjing University of Science & Technology Pattern Recognition:Statistical and Neural Lonnie C. Ludeman Lecture 7 Sept 23, 2005

  2. Review 1: Classifier Framework May be Optimum

  3. Review 2: Classifier performance Measures 1. A’Posteriori Probability (Maximize) 2. Probability of Error ( Minimize) 3. Bayes Average Cost (Maximize) 4. Probability of Detection ( Maximize with fixed Probability of False alarm) (Neyman Pearson Rule) 5. Losses (Minimize the maximum)

  4. Review 3: MAP and MPE Classification rule Form 1 C1 > If p( x | C1 ) P(C1 ) p( x | C2 ) P(C2 ) < C2 Form 2 C1 > If p( x | C1 ) / p( x | C2 ) P(C2 ) / P(C1 ) < C2 Likelihood ratio Threshold

  5. Topics for Lecture 7 • Bayes Decision Rule – Introduction 2-Class case • 2. Bayes Decision Rule – Derivation 2-Class Case • 3. General Calculation of Probability of Error • 4. Calculation of Bayes Risk

  6. Motivation ? Good Bad Select One Basket of Eggs Some good Some bad

  7. Possible Decision Outcomes: Decide a good egg is good good egg is bad bad egg is bad bad egg is good No problem - Cost = 0 Throw away - Cost = 1 y Throw away – Cost = 0.1 y Catastrophy ! – Cost = 100 y

  8. 1. Bayes Classifier- Statistical Assumptions (Two Class Case) Known: C1 : x ~ p(x | C1) , P(C1) C2 : x ~ p(x | C2) , P(C2) Classes Observed Pattern Vector ConditionalProbabilityDensity Functions A’Priori Probabilities

  9. Bayes Classifier - Cost definitions Define Costs associated with decisions: C11 C12 C21 C22 Where C = the cost associated with deciding Class C when true class Class C i j i j

  10. Bayes Classifier - Risk Definition Risk is defined as the average cost associated with making a decision. R = Risk = P(decide C1 | C1) P(C1) C11 + P(decide C1 | C2) P(C2) C12 + P(decide C2 | C1) P(C1) C21 + P(decide C2 | C2) P(C2) C22

  11. Bayes Classifier - Optimum Decision Rule Bayes Decision Rule selects regions R1 and R2, for deciding C1 and C2 respectively, to minimize the Risk, which is the average cost associated with making a decision. Can prove, details in book that the Bayes decision rule is a Likelihood Ratio Test (LRT) C1 p( x | C1) > (C22 - C12 ) P(C2) If = NBAYES < p( x | C2) (C11 - C21 ) P(C1) C2

  12. Bayes Classifier - Calculation of Risk

  13. Bayes Classifier - Special Case C11 = C22 = 0 cost of 0 for C12 = C21 =1 cost of 1 for Then Bayes Decision rule is equivalent to the Minimum Probability of Error Decision Rule correct classification incorrect classification

  14. Since C1 p( x | C1) > (C22 - C12 ) P(C2) If = NBAYES < p( x | C2) (C11 - C21 ) P(C1) C2 Reduces to C1 p( x | C1) > (1 - 0) P(C2) If = NMPE < p( x | C2) (1 - 0) P(C1) C2

  15. Bayes Decision Rule - Example Given the following Statistical Information p(x | C1) = exp(-x) u(x) P(C1) = 1/3 p(x | C2) = 2 exp(-2x) u(x) P(C2) = 2/3 Given the following Cost Assignment C11 = 0 C22 = 0 C12 = 3 C21 = 2 • Determine Bayes Decision Rule (Minimum Risk) • Simplify your test to the observation space • Calculate Bayes Risk for Bayes Decision Rule

  16. Bayes Example – Solution is LRT C1 p( x | C1) > NBAYES If < p( x | C2) C2 (C22 - C12 ) P(C2) (0 - 3 ) 2/3 NBAYES = = = 3 (C11 - C21 ) P(C1) (0 - 2 ) 1/3 p( x | C1) exp(-x) u(x) = ½ exp(x) u(x) = p( x | C2) 2exp(-2x)

  17. Bayes Example – Solution in different spaces (a) For x > 0 the Bayes Decision Rule is = C1 > In Likelihood Ratio Space If ½ exp(x) 3 < C2 (b) For x > 0 the equivalent decision rule in the observation space is seen to be = C1 In Observation Space > If x ln(6) < C2

  18. Bayes Example – Calculation of Bayes Risk (c) Must compute the conditional probabilities of error P(error | C1) = P(decide C2 |C1) = p( x| C1 ) dx R2 ln(6) = exp(-x) u(x) 0 = 5/6

  19. Bayes Example – Calculation of Bayes Risk (cont) P(error | C2) = P(decide C1 |C2) = p( x| C2 ) dx R1 o o = 2exp(-2x) u(x) ln(6) = 1/36

  20. Bayes Example – Calculation of Bayes Risk (cont) Risk = 0 + P(decide C2 | C1) P(C1) C21 + P(decide C1 | C2) P(C2) C12 +0 = (5/6) (1/3) 2 + (1/36) (2/3) 3 Risk = 11/18 units /decision

  21. 2. General Calculation of Probability of Error F2 decide C2 F1 decide C1 R1 decide C1 y = g(x) y Feature Space x p(x | C1) L( x ) = Pattern Space p(x | C2) R2 decide C2 N = Threshold L1 decide C1 0 L1 decide C1 Likelihood Ratio Space

  22. Probability of Error – Observation Space P(error) = p(error | C1) P(C1) + P(error | C2) P(C2) P(error | C1) = p(x| C1 ) dx P(error | C2) = p( x | C2 ) dx R2 R1

  23. Probability of Error – Feature Space P(error) = p(error | C1) P(C1) + P(error | C2) P(C2) P(error | C1) = p(y| C1 ) dy P(error | C2) = p( y | C2 ) dy F2 F1

  24. Probability of Error – Likelihood Ratio Space P(error) = p(error | C1) P(C1) + P(error | C2) P(C2) P(error | C1) = p(l| C1 ) dl P(error | C2) = p( l | C2 ) dl N - o o o o N

  25. End of Lecture 7

More Related