1. PART 5 Supervised Hebbian Learning

2. Outline Linear Associator The Hebb Rule Pseudoinverse Rule Application

3. Hebb ’ s Postulate “When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A’s efficiency, as one of the cells firing B, is increased.” D. O. Hebb, 1949 A B

4. Linear Associator

5. Hebb Rule(1/2)

6. Hebb Rule(2/2)

7. Batch Operation

8. Performance Analysis(1/2)

9. Performance Analysis(2/2)

10. Example(1/2)

11. Example(2/2)

12. Pseudoinverse Rule(1/2)

13. Pseudoinverse Rule(2/2)

14. Relationship to the Hebb Rule

16. Example

17. Autoassociative Memory

18. Tests 50% Occluded 67% Occluded Noisy Patterns (7 pixels)

19. Variations of Hebbian Learning Basic Rule: Learning Rate: Smoothing: Delta Rule: Unsupervised:

20. Solved Problems

22. Solution:

23. Solved Problems

27. Solution :

28. Solved Problems

35. Solution:

36. Solved Problems