Differential Perceptrons

As originally proposed, perceptrons were machines that scanned a discrete retina and combined the data gathered in a linear fashion to make decisions about the figure presented on the retina. This paper considers differential perceptions, which view a continuous retina. Thus, instead of summing the...

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Main Authors: Brooks, Martin, Ginsparg, Jerrold
Language:en_US
Published: 2004
Online Access:http://hdl.handle.net/1721.1/5804
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author Brooks, Martin
Ginsparg, Jerrold
author_facet Brooks, Martin
Ginsparg, Jerrold
author_sort Brooks, Martin
collection MIT
description As originally proposed, perceptrons were machines that scanned a discrete retina and combined the data gathered in a linear fashion to make decisions about the figure presented on the retina. This paper considers differential perceptions, which view a continuous retina. Thus, instead of summing the results of predicates, we must now integrate. This involves setting up a predicate space which transforms the typical perceptron sum, Ea(p)a(f), into Esacp,f(p)dp, where f is the figure on the retina, i.e. in the differential case, the figure is viewed as a function on the predicate space. We show that differential perceptrons are equivalent to perceptrons on the class of figures that fit exactly onto a sufficiently small square grid. By investigating predicates of various geometric transformations, we discover that translation and symmetry can be computed in finite order using finite coefficients in both continuous and discrete cases. We also note that in the perceptron scheme, combining data linearly implies the ability to combine data in a polynomial fashion.
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spelling mit-1721.1/58042019-04-09T15:24:51Z Differential Perceptrons Brooks, Martin Ginsparg, Jerrold As originally proposed, perceptrons were machines that scanned a discrete retina and combined the data gathered in a linear fashion to make decisions about the figure presented on the retina. This paper considers differential perceptions, which view a continuous retina. Thus, instead of summing the results of predicates, we must now integrate. This involves setting up a predicate space which transforms the typical perceptron sum, Ea(p)a(f), into Esacp,f(p)dp, where f is the figure on the retina, i.e. in the differential case, the figure is viewed as a function on the predicate space. We show that differential perceptrons are equivalent to perceptrons on the class of figures that fit exactly onto a sufficiently small square grid. By investigating predicates of various geometric transformations, we discover that translation and symmetry can be computed in finite order using finite coefficients in both continuous and discrete cases. We also note that in the perceptron scheme, combining data linearly implies the ability to combine data in a polynomial fashion. 2004-10-01T20:37:31Z 2004-10-01T20:37:31Z 1973-01-01 AIM-275 http://hdl.handle.net/1721.1/5804 en_US AIM-275 24 p. 8982882 bytes 669129 bytes application/postscript application/pdf application/postscript application/pdf
spellingShingle Brooks, Martin
Ginsparg, Jerrold
Differential Perceptrons
title Differential Perceptrons
title_full Differential Perceptrons
title_fullStr Differential Perceptrons
title_full_unstemmed Differential Perceptrons
title_short Differential Perceptrons
title_sort differential perceptrons
url http://hdl.handle.net/1721.1/5804
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