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Abstract: A constructive learning algorithm is described that builds a feedforward neural network with an optimal number of hidden units to balance convergence and generalization. The method starts with a small training set and a small network, and expands the training set incrementally after training. If the training does not converge, the network grows incrementally to increase its learning capacity. This process, called selective learning with flexible neural architectures (self), results in a... (Update)
Context of citations to this paper: More
.... the connection would disappear unless reinforced [20] Selective Learning with Flexible Neural Architectures (SELF) was introduced by [47]. It works on both the data and the network structure. Starting with a small training set and a small feed forward network, the training...
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BibTeX entry: (Update)
Zhang,B. T. \An incremental learning algorithm that optimizes network size and sample size in one trial", Proc. of ICNN'94(Orlando, Florida), pp.215-220, (June 28 - July 2, 1994) . http://citeseer.ist.psu.edu/article/zhang94incremental.html More
@misc{ zhang94incremental,
author = "B. Zhang",
title = "An incremental learning algorithm that optimizes network size and sample
size in one trial",
text = "Zhang,B. T. \An incremental learning algorithm that optimizes network size
and sample size in one trial, Proc. of ICNN'94(Orlando, Florida), pp.215-220,
(June 28 - July 2, 1994) .",
year = "1994",
url = "citeseer.ist.psu.edu/article/zhang94incremental.html" }
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13 Neural networks that teach themselves through genetic discov.. (context) - Zhang, Veenker - 1991
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10 Learning by Genetic Neural Evolution (context) - Zhang - 1992
8 Focused incremental learning for improved generalization wit.. (context) - Zhang, Veenker - 1991
6 The Vapnik-Chervonenkis dimension: information versus comple.. (context) - Abu-Mostafa - 1989
5 Self-development learning: Constructing optimal size neural .. (context) - Zhang - 1993
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