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Keywords
(7)
Adaptive System
Back Propagation
Error Propagation
Gradient Descent Method
Learning Networks
Local Minima
Gradient Descent
Related Publications
(343)
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Learning internal representations by error propagation
Learning internal representations by error propagation,David E. Rumelhart,Geoffrey E. Hinton,Ronald J. Williams
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Learning internal representations by error propagation
(
Citations: 6590
)
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David E. Rumelhart
,
Geoffrey E. Hinton
,
Ronald J. Williams
This paper presents a generalization of the perception learning procedure for learning the correct sets of connections for arbitrary networks. The rule, falled the generalized delta rule, is a simple scheme for implementing a
gradient descent method
for finding weights that minimize the sum squared error of the sytem's performance. The major theoretical contribution of the work is the procedure called error propagation, whereby the gradient can be determined by individual units of the network based only on locally available information. The major empirical contribution of the work is to show that the problem of
local minima
not serious in this application of gradient descent. Keywords: Learning; networks; Perceptrons; Adaptive systems; Learning machines; and
Back propagation
Conference:
Symposium on Parallel and Distributed Processing  SPDP
, 1986
Cumulative
Annual
Citation Context
(3150)
...A neural network consists of some basic components called neurons [
33
]...
Jingpeng Li
,
et al.
A pattern recognition based intelligent search method and two assignme...
...
1986
), which is based on an optimisation problem that looks for a set of network parameters, specifically weights, with the end result of obtaining the best classification performance (Mazurowski
et al
...
Elena EscrigOlmedo
,
et al.
Using fuzzy logic and neural networks to classify socially responsible...
...
Rumelhart et al. [1986
] proposed backpropagation neural networks using a generalized delta rule as a learning rule in training processes...
YieRuey Chen
,
et al.
Evaluation of Soil Liquefaction Potential Based on the Nonlinear Ener...
...) Both networks are trained using the standard backpropagation algorithm (
Rumelhart et al, 1986
)...
Gregory A. Caza
,
et al.
Pragmatic Bootstrapping: A Neural Network Model of Vocabulary Acquisit...
...The first focuses on gradientbased techniques to train all the parameters in the network, of which the bestknown example is backpropagation through time (Rumelhart, Hinton, & Williams,
1986
)...
Michiel Hermans
,
et al.
Recurrent Kernel Machines: Computing with Infinite Echo State Networks
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Citations
(6590)
A pattern recognition based intelligent search method and two assignment problem case studies
(
Citations: 1
)
Jingpeng Li
,
Edmund K. Burke
,
Rong Qu
Published in 2012.
Using fuzzy logic and neural networks to classify socially responsible organisations
Elena EscrigOlmedo
,
M. Ángeles FernándezIzquierdo
,
Idoya FerreroFerrero
,
Raúl LeónSoriano
,
M. Jesús MuñozTorres
,
Juana M. RiveraLirio
Journal:
Journal of Environmental Planning and Management  J ENVIRON PLAN MANAG
, vol. aheadofp, no. aheadofp, pp. 116, 2012
Evaluation of Soil Liquefaction Potential Based on the Nonlinear Energy Dissipation Principles
YieRuey Chen
,
JingWen Chen
,
ShunChieh Hsieh
,
YiTeng Chang
Journal:
Journal of Earthquake Engineering  J EARTHQU ENG
, vol. justaccep, no. justaccep, 2012
Pragmatic Bootstrapping: A Neural Network Model of Vocabulary Acquisition
Gregory A. Caza
,
Alistair Knott
Journal:
Language Learning and Development
, vol. 8, no. 2, pp. 113135, 2012
Recurrent Kernel Machines: Computing with Infinite Echo State Networks
Michiel Hermans
,
Benjamin Schrauwen
Journal:
Neural Computation  NECO
, vol. 24, no. 1, pp. 104133, 2012