Journal ArticleDOI
Initializing back propagation networks with prototypes
Thierry Denoeux,Régis Lengellé +1 more
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TLDR
Simulation results are presented, showing that initializing back propagation networks with prototypes generally results in drastic reductions in training time, improved robustness against local minima, and better generalization.About:
This article is published in Neural Networks.The article was published on 1993-03-06. It has received 138 citations till now. The article focuses on the topics: Feature vector & Backpropagation.read more
Citations
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Journal ArticleDOI
Neural networks in geophysical applications
TL;DR: Techniques are described for faster training, better overall performance, i.e., generalization, and the automatic estimation of network size and architecture.
Journal ArticleDOI
A big data urban growth simulation at a national scale: Configuring the GIS and neural network based Land Transformation Model to run in a High Performance Computing (HPC) environment
Bryan C. Pijanowski,Amin Tayyebi,Jarrod Doucette,Burak K. Pekin,David Braun,James D. Plourde +5 more
TL;DR: An overview of a redesigned LTM capable of running at continental scales and at a fine (30m) resolution using a new architecture that employs a windows-based High Performance Computing (HPC) cluster is provided.
Proceedings ArticleDOI
Learning multidimensional signal processing
TL;DR: This paper presents the general strategy for designing learning machines as well as a number of particular designs based on two main principles: simple adaptive local models; and adaptive model distribution.
Journal ArticleDOI
A weight initialization method for improving training speed in feedforward neural network
Jim Y. F. Yam,Tommy W. S. Chow +1 more
TL;DR: The proposed method ensures that the outputs of neurons are in the active region and increases the rate of convergence and with the optimal initial weights determined, the initial error is substantially smaller and the number of iterations required to achieve the error criterion is significantly reduced.
References
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Book ChapterDOI
Learning internal representations by error propagation
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
Book
Learning internal representations by error propagation
TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.
Proceedings Article
The Cascade-Correlation Learning Architecture
TL;DR: The Cascade-Correlation architecture has several advantages over existing algorithms: it learns very quickly, the network determines its own size and topology, it retains the structures it has built even if the training set changes, and it requires no back-propagation of error signals through the connections of the network.