Connectionism [electronic resource] : a hands-on approach / Michael R.W. Dawson.
ConnectionISM is a "hands on" introduction to connectionist modeling. Three different types of connectionist architectures - distributed associative memory, perceptron, and multilayer perceptron - are explored. In an accessible style, Dawson provides a brief overview of each architecture,...
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| Online Access: |
Full Text (via Wiley) |
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| Main Author: | |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Oxford, UK ; Malden, MA :
Blackwell Pub.,
2005.
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| Edition: | 1st ed. |
| Subjects: |
Table of Contents:
- Ch. 1. Hands-on connectionism
- Ch. 2. The distributed associative memory
- Ch. 3. The James program
- Ch. 4. Introducing Hebb learning
- Ch. 5. Limitations of Hebb learning
- Ch. 6. Introducing the delta rule
- Ch. 7. Distributed networks and human memory
- Ch. 8. Limitations of delta rule learning
- Ch. 9. The perceptron
- Ch. 10. The Rosenblatt program
- Ch. 11. Perceptrons and logic gates
- Ch. 12. Performing more logic with perceptrons
- Ch. 13. Value units and linear nonseparability
- Ch. 14. Network by problem type interactions
- Ch. 15. Perceptrons and generalization
- Ch. 16. Animal learning theory and perceptrons
- Ch. 17. The multilayer perceptron
- Ch. 18. The Rumelhart program
- Ch. 19. Beyond the perceptron's limits
- Ch. 20. Symmetry as a second case study
- Ch. 21. How many hidden units?
- Ch. 22. Scaling up with the parity problem
- Ch. 23. Selectionism and parity
- Ch. 24. Interpreting a small network.
- Ch. 25. Interpreting networks of value units
- Ch. 26. Interpreting distributed representations
- Ch. 27. Creating your own training sets.