Cascade-Correlation learning architecture

E474908

constructive neural network algorithm neural network training method supervised learning algorithm

Cascade-Correlation learning architecture is a neural network training method that incrementally builds its own topology by adding new hidden units during learning to improve performance.

Try in SPARQL Jump to: Surface forms Statements Referenced by

Observed surface forms (2)

Surface form	Occurrences
Cascade-Correlation learning algorithm	1
Cascade-Correlation neural network architecture	1

Statements (46)

Predicate	Object
instanceOf	constructive neural network algorithm ⓘ neural network training method ⓘ supervised learning algorithm ⓘ
abbreviation	CasCor NERFINISHED ⓘ Cascade-Correlation NERFINISHED ⓘ
addsHiddenUnits	one at a time ⓘ
architectureProperty	topology determined during training rather than fixed a priori ⓘ
category	adaptive network architecture method ⓘ constructive learning algorithm ⓘ
comparedWith	backpropagation ⓘ
describedIn	The Cascade-Correlation Learning Architecture NERFINISHED ⓘ
designedToImprove	generalization performance ⓘ training speed compared to standard backpropagation ⓘ
developedBy	Christian Lebiere NERFINISHED ⓘ Scott E. Fahlman NERFINISHED ⓘ
field	machine learning ⓘ neural networks ⓘ
hasAuthor	Christian Lebiere NERFINISHED ⓘ Scott E. Fahlman NERFINISHED ⓘ
hasKeyIdea	adds new hidden units during training ⓘ constructive growth of network architecture ⓘ freezes weights of previously learned units ⓘ incrementally builds its own network topology ⓘ new hidden units are trained to maximize correlation with residual error ⓘ
hasLearningParadigm	supervised learning ⓘ
hasTrainingPhase	candidate unit training phase ⓘ output weight training phase ⓘ
hiddenUnitConnectionPattern	new hidden units connect to all existing network units ⓘ
influenced	constructive neural network methods ⓘ growing neural network architectures ⓘ
initialTopology	network starts with no hidden units ⓘ
introducedIn	1990 ⓘ
languageOfOriginalPublication	English ⓘ
networkType	feedforward neural network ⓘ
optimizationTarget	correlation between candidate unit output and network residual error ⓘ
outputLayerTraining	output weights are retrained after adding each new hidden unit ⓘ
publicationVenue	Advances in Neural Information Processing Systems NERFINISHED ⓘ
stoppingCriterion	growth stops when performance no longer improves ⓘ
supports	incremental learning of network structure ⓘ
trainingObjective	reduce network error by adding hidden units ⓘ
usedFor	classification ⓘ function approximation ⓘ regression ⓘ
usesOptimizationCriterion	maximization of correlation between unit output and network residual error ⓘ
usesWeightFreezing	true ⓘ
yearOfFirstPublication	1990 ⓘ

Referenced by (3)

Full triples — surface form annotated when it differs from this entity's canonical label.

Scott Fahlman → knownFor → Cascade-Correlation learning architecture ⓘ

Scott Fahlman → developed → Cascade-Correlation learning architecture ⓘ

this entity surface form: Cascade-Correlation neural network architecture

Scott Fahlman → notableWork → Cascade-Correlation learning architecture ⓘ

this entity surface form: Cascade-Correlation learning algorithm