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- Oja's_rule abstract "Oja's learning rule, or simply Oja's rule, named after a Finnish computer scientist Erkki Oja, is a model of how neurons in the brain or in artificial neural networks change connection strength, or learn, over time. It is a modification of the standard Hebb's Rule (see Hebbian learning) that, through multiplicative normalization, solves all stability problems and generates an algorithm for principal components analysis. This is a computational form of an effect which is believed to happen in biological neurons.".
- Oja's_rule wikiPageExternalLink oja.
- Oja's_rule wikiPageExternalLink Oja_learning_rule.
- Oja's_rule wikiPageID "9617564".
- Oja's_rule wikiPageRevisionID "602063037".
- Oja's_rule hasPhotoCollection Oja's_rule.
- Oja's_rule subject Category:Biophysics.
- Oja's_rule subject Category:Neural_networks.
- Oja's_rule subject Category:Neuroscience.
- Oja's_rule type Abstraction100002137.
- Oja's_rule type Communication100033020.
- Oja's_rule type ComputerArchitecture106725249.
- Oja's_rule type Description106724763.
- Oja's_rule type Message106598915.
- Oja's_rule type NeuralNetwork106725467.
- Oja's_rule type NeuralNetworks.
- Oja's_rule type Specification106725067.
- Oja's_rule type Statement106722453.
- Oja's_rule comment "Oja's learning rule, or simply Oja's rule, named after a Finnish computer scientist Erkki Oja, is a model of how neurons in the brain or in artificial neural networks change connection strength, or learn, over time. It is a modification of the standard Hebb's Rule (see Hebbian learning) that, through multiplicative normalization, solves all stability problems and generates an algorithm for principal components analysis.".
- Oja's_rule label "Oja's rule".
- Oja's_rule sameAs m.02pl_kv.
- Oja's_rule sameAs Q7081581.
- Oja's_rule sameAs Q7081581.
- Oja's_rule sameAs Oja's_rule.
- Oja's_rule wasDerivedFrom Oja's_rule?oldid=602063037.
- Oja's_rule isPrimaryTopicOf Oja's_rule.