needed: complexity analyses of NN & evolutionary learning systems

[Matthew Brand]

I am looking for proofs of complexity limits for tasks learnable by
(multilayer) PDP algorithms.  Specifically, the proof that the
generalized delta rule (aka backprop) constrains one to linearly
independent association tasks.  Similar work on Boltzmann machines or
any simulated annealing-based learning algorithm would be even more
welcome.  And, as long as I'm writing my wish list, if you know of any
work which has indicated strong upper bounds on the tasks complexity
of 3+ layer nets configured via energy-minimization algorithms, I'm
mighty keen to see it.

Other requests: references, tech reports, or reprints on

      -	Metropolis algorithm-based learning rules other than the
	Boltzmann machine.

      -	Augmentations of the generalized delta rule, specifically
	relaxations of the feed-forward constraint, for example
	approximations of error in recurrent subnets.  I understand
	that a researcher in Spain is doing very interesting stuff
	along these lines.

      -	Complexity analyses of genetic learning algorithms such
	as Holland's classifier systems:  assuming parallel
	operation, how does performance decline with scale-up
	of # of rules, # of condition bits, and size of the
	message list?

Please mail responses to me; I'll summarize and post.  I'd like to do
this quickly; in 4 weeks I'll be taking a vacation far away from
computers, and I'm some distance from a library suitable to look up
computer science or AI references.  For these reasons, papers sent
e-mail or US-mail would be much appreciated.

My address:		Matthew Brand
			5631 S. Kenwood Ave. #2B
			Chicago, IL., 60637.1739

Many thanks in advance.

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   *    *   *      *				matthew brand
                 *   *    *			meb at oddjob.uchicago.edu
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