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From: loren@dweasel.llnl.gov (Loren Petrich)
Newsgroups: comp.ai.neural-nets
Subject: NN Benchmarks
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Date: 31 May 91 23:19:14 GMT
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	Here is a list of benchmarks for NN's that I have learned of.

	XOR: a classic. It demonstrates the need for nonlinearity.

	    0 1
	  +----
	0 | 0 1
	1 | 1 0

	Parity: is a binary string odd or even? It has the difficulty
that the two outcomes are intimately intertwined.

	Encoder: an auto-associative problem (output = input
intended). Solve for 100..0, 010..0, ..., 000..1, or some equivalent.

	Multiplexor: essentially the same thing?

	2 out of 3: if correct, return true, otherwise, return false.
This seems like a linear problem.

	Fahlman presents the following ones:

	Zig-zag and Two Spirals.

	Both of them have as their inputs points in 2-space and
outputs the associated values. Fahlman in some papers of his (in
/pub/neuroprose at cheops.cis.ohio-state.edu, avail. by anon. FTP)
uses a filled-up plane of points in the 2-space with appropariate
values as a test set; he graphs the result.

	His training set for Two Spirals consists of two intertwined
spirals of points of opposite colors. His test set is all the area in
between the points; as the training proceeds with his algorithm, one
can see the filled-in spirals take place in the graphs in his paper.

	I think that this type of display may be very useful in
displaying the generalization capabilities of various NN algorithms.
One can represent more than one output with colors or textures on the
graph. Any comments?


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Loren Petrich, the Master Blaster: loren@sunlight.llnl.gov

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