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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… 
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Related topics

Related topics

8 relations
Activation functionBackpropagationCatastrophic interferenceFeedforward neural network

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2015
2015
Neural Functions of the Delta-Opioid Receptor
The delta-opioid receptor (DOR) was traditionally thought to be primarily involved in modulating the transmission of messages… 
2012
2012
An enhanced relevance criterion for more concise supervised pattern discovery
Supervised local pattern discovery aims to find subsets of a database with a high statistical unusualness in the distribution of… 
2002
2002
Learning for automatic personalization in a semantic taxonomy-based meta-search agent
Highly Cited
2001
Highly Cited
2001
NeuroFAST : On-Line Neuro-Fuzzy ART-Based Structure and Parameter Learning TSK Model
NeuroFAST is an on-line fuzzy modeling learning algorithm, featuring high function approximation accuracy and fast convergence… 
1998
1998
Aspects of network training and validation on noisy data - Part 1. Training aspects
Highly Cited
1996
Highly Cited
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… 
Highly Cited
1994
Highly Cited
1994
Object-oriented backpropagation and its application to structural design
1993
1993
Hierarchical artificial neural networks for edge enhancement
1993
1993
Divide and Conquer Neural Networks
1990
1990
STATISTICAL PATTERN RECOGNITION REVISITED
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