http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Neural network learning and expert systems
1. Introduction and important definitions -- 2. Representation issues -- 3. Perceptron learning and the pocket algorithm -- 4. Winner-take-all groups or linear machines -- 5. Autoassociators and one-shot learning -- 6. Mean squared error (MSE) algorithms -- 7. Unsupervised learning -- 8. The distributed method and radial basis functions -- 9. Computational learning theory and the BRD algorithm -- 10. Constructive algorithms -- 11. Backpropagation -- 12. Backpropagation : variations and applications -- 13. Simulated annealing and boltzmann machines -- 14. Expert systems and neural networks -- 15. Details of the MACIE system -- 16. Noise, redundancy, fault detection, and bayesian decision theory -- 17. Extracting rules from networks.
Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty univerzity
Includes bibliographical references (p. [349]-359) and index.