1. Associative Learning

2. Simple Associative Network

3. Banana Associator

4. Unsupervised Hebb Rule

5. Banana Recognition Example

6. Example

7. Problems with Hebb Rule
Weights can become arbitrarily large
There is no mechanism for weights to decrease

8. Hebb Rule with Decay

9. Example: Banana Associator

10. Example

11. Problem of Hebb with Decay

12. Instar (Recognition Network)

13. Instar Operation

14. Vector Recognition

15. Instar Rule

16. Graphical Representation

17. Example

18. Training

19. Further Training

20. Kohonen Rule

21. Outstar (Recall Network)

22. Outstar Operation

23. Outstar Rule

24. Example - Pineapple Recall

25. Definitions

26. Iteration 1

27. Convergence

28. Boltzmann Learning
Stochastic learning process with a recurrent structure
State of a neuron is +1 or –1 and some neurons are free (adaptive state) and others are clamped (frozen state)
Boltzmann machine is characterized by an energy function
Free neurons change state with probability:
The learning rule is given by:
Where r+kj is the correlation with neurons in clamped states and r-kj is the correlation with the neurons in a frozen state
Hidden
Z-1
Delay
Visible
Clamped