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Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory Statistical Decision Making.

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Presentation on theme: "Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory Statistical Decision Making."— Presentation transcript:

1 Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory Statistical Decision Making Supervised Learning: Using a training set to design classifier – Using a separate test set for accuracy Unsupervised Learning: clustering Parametric decision making: probability density function is known for each class, not the parameters (mean, variance) – must be estimated. Pattern Recognition1

2 Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory Pattern Recogntion2

3 Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory Pattern Recgntion3

4 Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory Pattern Recgntion4

5 Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory Pattern Recgntion5

6 Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory Pattern Recgntion6

7 Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory Bayesian decision making refers to choosing the most likely class, given the value of the feature(s) P(x/C) is the conditional probability of obtaining feature x given that the sample is from class C P(C/x) = P(C) P(x/C) P(x) Example: What is the probability that a person has a cold (C) given that he or she has a fever (f) P(C) =0.01, P(f)=0.02, P(f/C)=0.04 P(C/f) = P(C) P(f/C) = (0.01)(0.4) = 0.2 P(f) 0.02 Pattern Recgntion7

8 Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory Pattern Recgntion8

9 Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory Pattern Recgntion9

10 Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory Pattern Recgntion10

11 Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory Pattern Recgntion11

12 Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory Pattern Recgntion12

13 Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory Pattern Recgntion13

14 Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory Pattern Recgntion14

15 Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory Pattern Recogntion15

16 Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory Likelihood Ratio between class C i and C i R = P(C i /x) = P(C i ) P(x/C i ) P(C j /x) P(C j ) P(x/C j ) Likelihood Ratio between class A and B R = P(A /x) = P(A) P(x/A) P(B/x) P(B) P(x/B) If R>1 – select class A If R<1 – select class B Pattern Recgntion16

17 Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory Example: Detecting the HIV virus using the ELISA test H – patient has HIV virus H’ – patient does not have HIV virus Pos – patient tests positive Neg – patient tests negative Let: P(H)=0.15  P(H’)=0.85 P(Pos/H) = 0.95 and P(Pos/H’)=0.02 Pattern Recgntion17

18 Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory Bayes’ Theorem P(H/Pos) = P(H) P(Pos/H), P(H) P(Pos/H)+P(H’) P(Pos/H’) = (0.15)(0.95) = 0,893 (0.15)(0.95) + (0.85)(0.02) P(H/Pos)>0.5 Likelihood Ratio R = P(H) P(Pos/H) = (0.15)(0.95) = 8.382 P(H’) P(Pos/H’) (0.85)(0.02) R>1 Pattern Recgntion18

19 Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory Pattern Recgntion19

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