Addressing the current tension within the artificial intelligence
community between advocates of powerful symbolic representations
that lack efficient learning procedures and advocates of relatively
simple learning procedures that lack the ability to represent
complex structures effectively. The six contributions in
Connectionist Symbol Processing address the current tension within
the artificial intelligence community between advocates of powerful
symbolic representations that lack efficient learning procedures
and advocates of relatively simple learning procedures that lack
the ability to represent complex structures effectively. The
authors seek to extend the representational power of connectionist
networks without abandoning the automatic learning that makes these
networks interesting.Aware of the huge gap that needs to be
bridged, the authors intend their contributions to be viewed as
exploratory steps in the direction of greater representational
power for neural networks. If successful, this research could make
it possible to combine robust general purpose learning procedures
and inherent representations of artificial intelligence-a synthesis
that could lead to new insights into both representation and
learning.
General
Is the information for this product incomplete, wrong or inappropriate?
Let us know about it.
Does this product have an incorrect or missing image?
Send us a new image.
Is this product missing categories?
Add more categories.
Review This Product
No reviews yet - be the first to create one!
Welcome to Loot.co.za!
Sign in / Register |Wishlists & Gift Vouchers |Help | Advanced search
