Search
Login
Content
Journal info
Publish
Published: 09 October 1986
Learning representations by back-propagating errors
David E. Rumelhart, Geoffrey E. Hinton & Ronald J. Williams 
Nature  323, 533–536(1986)Cite this article
48k Accesses
10119 Citations
221 Altmetric
Metrics
Abstract
We describe a new learning procedure, back-propagation, for networks of neurone-like units. The procedure repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector. As a result of the weight adjustments, internal ‘hidden’ units which are not part of the input or output come to represent important features of the task domain, and the regularities in the task are captured by the interactions of these units. The ability to create useful new features distinguishes back-propagation from earlier, simpler methods such as the perceptron-convergence procedure1.
Access through your institution
Buy or subscribe
Access options
Subscribe to Journal
Get full journal access for 1 year
$199.00
only $3.90 per issue
Subscribe
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
from$8.99
Rent or Buy
All prices are NET prices.
Additional access options:
Log in
Access through your institution
Learn about institutional subscriptions
References
1
Rosenblatt, F. Principles of Neurodynamics (Spartan, Washington, DC, 1961).
Google Scholar
 
2
Minsky, M. L. & Papert, S. Perceptrons (MIT, Cambridge, 1969).
MATH
 
Google Scholar
 
3
Le Cun, Y. Proc. Cognitiva 85, 599–604 (1985).
Google Scholar
 
4
Rumelhart, D. E., Hinton, G. E. & Williams, R. J. in Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Vol. 1: Foundations (eds Rumelhart, D. E. & McClelland, J. L.) 318–362 (MIT, Cambridge, 1986).
Book
 
Google Scholar
 
Download references
Author information
Affiliations
Institute for Cognitive Science, C-015, University of California, San Diego, La Jolla, California, 92093, USA
David E. Rumelhart & Ronald J. Williams
Department of Computer Science, Carnegie-Mellon University, Pittsburgh, Philadelphia, 15213, USA
Geoffrey E. Hinton
Rights and permissions
Reprints and Permissions
About this article
Cite this article
Rumelhart, D., Hinton, G. & Williams, R. Learning representations by back-propagating errors. Nature 323, 533–536 (1986). https://doi.org/10.1038/323533a0
Download citation
Received
01 May 1986
Accepted
31 July 1986
Issue Date
09 October 1986
DOI
https://doi.org/10.1038/323533a0
Further reading
A neural network-based algorithm for high-throughput characterisation of viscoelastic properties of flowing microcapsules
Tao Lin, Zhen Wang[…] & Yi Sui
Soft Matter (2021)
Predicting Emotional States Using Behavioral Markers Derived From Passively Sensed Data: Data-Driven Machine Learning Approach
Emese Sükei, Agnes Norbury[…] & Antonio Artés
JMIR mHealth and uHealth (2021)
Suspended sediment yield modeling in Mahanadi River, India by multi-objective optimization hybridizing artificial intelligence algorithms
Arvind Yadav, Snehamoy Chatterjee & Sk Md Equeenuddin
International Journal of Sediment Research (2021)
Modelling habitat suitability of the Indo-Pacific humpback dolphin using artificial neural network: The influence of shipping
Mingli Lin, Mingming Liu[…] & Songhai Li
Ecological Informatics (2021)
Detection of foraging behavior from accelerometer data using U-Net type convolutional networks
Mạnh Cường Ngô, Raghavendra Selvan[…] & Susanne Ditlevsen
Ecological Informatics (2021)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.
Nature ISSN 1476-4687 (online)
About us
Press releases
Press office
Contact us
Discover content
Journals A-Z
Articles by subject
Nano
Protocol Exchange
Nature Index
Publishing policies
Nature portfolio policies
Open access
Author & Researcher services
Reprints & permissions
Research data
Language editing
Scientific editing
Nature Masterclasses
Nature Research Academies
Libraries & institutions
Librarian service & tools
Librarian portal
Open research
Recommend to library
Advertising & partnerships
Advertising
Partnerships & Services
Media kits
Branded content
Career development
Nature Careers
Nature Conferences
Nature events
Regional websites
Nature Africa
Nature China
Nature India
Nature Italy
Nature Japan
Nature Korea
Nature Middle East
Legal & Privacy
Privacy Policy
Use of cookies
Manage cookies/Do not sell my data
Legal notice
Accessibility statement
Terms & Conditions
California Privacy Statement
© 2021 Springer Nature Limited
Your privacy
We use cookies to make sure that our website works properly, as well as some "optional" cookies to personalise content and advertising, provide social media features and analyse how people use our site. By accepting some or all optional cookies you give consent to the processing of your personal data, including transfer to third parties, some in countries outside of the European Economic Area that do not offer the same data protection standards as the country where you live. You can decide which optional cookies to accept by clicking on "Manage Settings", where you can also find more information about how your personal data is processed.View our privacy policy
natureletters