From: Philip Goetz (email@example.com)
Date: Wed Feb 01 2006 - 10:40:56 MST
On 1/28/06, Mitchell Porter <firstname.lastname@example.org> wrote:
> I have been unable to motivate the focus on n-spheres to my own
> satisfaction, but some adaptation of Douglas Matzke's "corob theory" *might*
> do it. Matzke talks about using random points in a high-dimensional
> hypercube as naming tokens; the higher the dimensionality, the better their
> properties from the perspective of error correction (greater spread, more
> uniform spread). *If* you were restricted to using uniformly normalized
> vectors drawn from a Euclidean space, you *might* find that unit 8-vectors
> (which form a 7-sphere) are optimal for a similar reason. But I cannot see
> that McKinstry himself made this argument anywhere.
Try doing a web search on "hypersphere classifier". Kohonen networks
are hypersphere classifiers.
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