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Delta rule

In machine learning, the delta rule is a gradient descent learning rule for updating the weights of the inputs to artificial neurons in a single… Expand
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Related topics

Related topics

8 relations
Activation functionBackpropagationCatastrophic interferenceFeedforward neural network
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Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2016
2016
An entropy-based early warning indicator for systemic risk
The purpose of this paper is the construction of an early warning indicator for systemic risk using entropy measures. The… Expand
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Highly Cited
2012
Highly Cited
2012
(Light) stop signs
A bstractStop squarks with a mass just above the top’s and which decay to a nearly massless LSP are difficult to probe because of… Expand
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Highly Cited
2009
Highly Cited
2009
Delta TFIDF: An Improved Feature Space for Sentiment Analysis
Mining opinions and sentiment from social networking sites is a popular application for social media systems. Common approaches… Expand
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2009
2009
Performance Comparison of Multi-layer Perceptron (Back Propagation, Delta Rule and Perceptron) algorithms in Neural Networks
A multilayer perceptron is a feedforward artificial neural network model that maps sets of input data onto a set of appropriate… Expand
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Highly Cited
2008
Highly Cited
2008
Sustainable biodiversity conservation in the Niger Delta: a practical approach to conservation site selection
The rich biodiversity repository of the Niger Delta region of Nigeria is under severe threat from diverse sources such as… Expand
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Highly Cited
2006
Highly Cited
2006
Effective sea-level rise and deltas: Causes of change and human dimension implications
An assessment is made of contemporary effective sea-level rise (ESLR) for a sample of 40 deltas distributed worldwide. For any… Expand
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1996
1996
Mapping Ecological Land Systems and Classification Uncertainties from Digital Elevation and Forest-Cover Data Using Neural Networks
Our approaches in this project emphasized mainly the technical aspects of the land-systems classification problem with neural… Expand
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Highly Cited
1992
Highly Cited
1992
Adapting Bias by Gradient Descent: An Incremental Version of Delta-Bar-Delta
Appropriate bias is widely viewed as the key to efficient learning and generalization. I present a new algorithm, the Incremental… Expand
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Highly Cited
1988
Highly Cited
1988
Increased rates of convergence through learning rate adaptation
WHILE THERE EXIST MANY TECHNIQUES FOR FINDING THE PARAMETERS THAT MINI- MIZE AN ERROR FUNCTION, ONLY THOSE METHODS THAT SOLELY… Expand
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Highly Cited
1987
Highly Cited
1987
Supervised Learning of Probability Distributions by Neural Networks
We propose that the back propagation algorithm for supervised learning can be generalized, put on a satisfactory conceptual… Expand
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