Multi-scale training of a large backpropagation net

E. W. Feiten; O. Martin; S. W. Otto; J. Hutchinson

doi:10.1007/BF00205112

Multi-scale training of a large backpropagation net

E. W. Feiten, O. Martin, S. W. Otto, J. Hutchinson

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

The shortcomings of automatic learning in neural networks can be overcome by incorporating features of the problem into the weight space. We teach a multi-layer perceptron to recognize hand-printed characters by using a multi-scale method. This leads to better performance for learning rates and for generalization than direct use of back-propagation.

Original language	English (US)
Pages (from-to)	503-509
Number of pages	7
Journal	Biological Cybernetics
Volume	62
Issue number	6
DOIs	https://doi.org/10.1007/BF00205112
State	Published - Apr 1990

All Science Journal Classification (ASJC) codes

Biotechnology
Computer Science(all)

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10.1007/BF00205112

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title = "Multi-scale training of a large backpropagation net",

abstract = "The shortcomings of automatic learning in neural networks can be overcome by incorporating features of the problem into the weight space. We teach a multi-layer perceptron to recognize hand-printed characters by using a multi-scale method. This leads to better performance for learning rates and for generalization than direct use of back-propagation.",

author = "Feiten, {E. W.} and O. Martin and Otto, {S. W.} and J. Hutchinson",

year = "1990",

month = apr,

doi = "10.1007/BF00205112",

language = "English (US)",

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pages = "503--509",

journal = "Biological Cybernetics",

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AU - Feiten, E. W.

AU - Martin, O.

AU - Otto, S. W.

AU - Hutchinson, J.

PY - 1990/4

Y1 - 1990/4

N2 - The shortcomings of automatic learning in neural networks can be overcome by incorporating features of the problem into the weight space. We teach a multi-layer perceptron to recognize hand-printed characters by using a multi-scale method. This leads to better performance for learning rates and for generalization than direct use of back-propagation.

AB - The shortcomings of automatic learning in neural networks can be overcome by incorporating features of the problem into the weight space. We teach a multi-layer perceptron to recognize hand-printed characters by using a multi-scale method. This leads to better performance for learning rates and for generalization than direct use of back-propagation.

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U2 - 10.1007/BF00205112

DO - 10.1007/BF00205112

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SP - 503

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JO - Biological Cybernetics

JF - Biological Cybernetics

IS - 6

ER -

Multi-scale training of a large backpropagation net

Abstract

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