1.  i=  2 I

2.  i=   i= 

3.  i=arbitrary

4. Complex decision boundaries

5. Machine Learning Design & Validation of Classifiers

6. Computer Vision Detection of Errors Sensor Object A/D Converter

7. Pattern of Data X1 X2 + + + + + + + + + + o o o o o o o o o o

8. Learning System Samples Learning System Classifiers

9. Classification Systems Data for classification Classifier Decision Pertaining to class

10. Design of a classifier Samples for training Values of variables (xi) Classes Pertaining to samples Learning System Classifier type Classifier for Specific application Case Variables (Features) Classes

11. Estimating the execution of a learning system What is an error? Reason for error (estimate) = number of errors number of cases Class (+) Class (-) Classification (+) Correct (+/+) Error (-/+) Classification (-) Error (+/-) Correct (-/-)

12. Apparent and true error Classifier Samples for training Apparent reason for error New cases True error

13. Error estimation using samples for training and samples for testing Cases for training the classifier Cases for testing the classifier Samples

14. Example: 1-d Class 1: n 1 = 5 X 1 Train Y 1 Train Class 2: n 1 = 5 X 2 Train Y 2 Test

15. Estimation of Parameters

16. Classification ML Rule Class 1

17. Classification ML Rule Class 2

18. A simpler Classification ML Rule Class 1

19. Classification ML Rule Class 2