Abstract
Despite the well-known performances and the theoretical power of neural networks, learning and generalizing are sometimes very difficult. In this article, we investigate how short term memories and forcing the agent to re-use its knowledge on-line can enhance the generalization capabilities. For this purpose, a system is described in a temporal framework, where communication skills are increased, thus enabling the teacher to supervise the way the agent “thinks”.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Christiansen, M.H., Curtin, S.L.: The power of statistical learning: No need for algebraic rules. In: Hahn, M., Stoness, S.C. (eds.) Proceedings of the Twenty first Annual Conference of the Cognitive Science Society, pp. 114–119. Erlbaum, Mahwah (1999)
Dominey, P.F., Lelekov, T., Ventre-Dominey, J., Jeannerod, M.: Dissociable processes for learning the surface and abstract structure of sensorimotor sequences. Journal of Cognitive Neuroscience 10, 734–751 (1998)
Dominey, P.F., Ramus, F.: Neural network processing of natural langage I: Sensitivity to serial, temporal and abstract structure of language in the infant. Language and Cognitive Processes 15(1), 87–127 (2000)
Johansson, U., Niklasson, L.: Predicting the impact of advertising - a neural network approach. In: The International Joint Conference on Neural Network (2001)
Marcus, G.F., Vijayan, S., Bandi Rao, S., Vishton, P.M.: Rule learning by seven month-old infants. Science 283, 77–80 (1999)
Ring, M.B.: CHILD: A first step towards continual learning. Machine Learning 28(1), 77–104 (1997)
Senjowski, T.J., Rosenberg, C.R.: Parallel networks that learn to pronounce English text. Complex Systems 1, 145–168 (1987)
Sutton, R., Barto, A.: Reinforcement learning. MIT Press, Cambridge (1995)
Vilalta, R., Drissi, Y.: A perspective view and survey of meta-learning. Artificial Intelligence Review 18(2), 77–95 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Orseau, L. (2005). Short Term Memories and Forcing the Re-use of Knowledge for Generalization. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_7
Download citation
DOI: https://doi.org/10.1007/11550907_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28755-1
Online ISBN: 978-3-540-28756-8
eBook Packages: Computer ScienceComputer Science (R0)