kadie@herodotus.cs.uiuc.edu (Carl M. Kadie) (12/20/89)
In article <15039@boulder.Colorado.EDU> bill@synapse.Colorado.EDU (Bill Skaggs) writes: ... > The secondary reason (IMHO) is that neural nets are massively parallel. >When one has reached the limits of sequential speed, one must go to >parallelism in order to get greater power. Neural nets are unlikely to >ever provide especially _elegant_ solutions to very many problems: their >virtue is that they provide a brutal and simplistic solution that sometimes >(surprisingly) actually works. ... Anyone interested in parallel machine learning systems should look at: Omohundro, S. (1987) Efficient algorithms with neural network behavior. Complex Systems, 1:273-347. He shows how to parallelize an ID3-like algorithm. Even when run on a serial machine, ID3 is much, much faster than most neural-inspired algorithms. Putting it on a parallel machine makes it faster still. Carl Kadie University of Illinois at Urbana-Champaign ARPA: kadie@m.cs.uiuc.edu