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- Structural_risk_minimization abstract "Structural risk minimization (SRM) is an inductive principle of use in machine learning. Commonly in machine learning, a generalized model must be selected from a finite data set, with the consequent problem of overfitting – the model becoming too strongly tailored to the particularities of the training set and generalizing poorly to new data. The SRM principle addresses this problem by balancing the model's complexity against its success at fitting the training data.The SRM principle was first set out in a 1974 paper by Vladimir Vapnik and Alexey Chervonenkis and uses the VC dimension.".
- Structural_risk_minimization wikiPageExternalLink srm.
- Structural_risk_minimization wikiPageID "10704974".
- Structural_risk_minimization wikiPageRevisionID "507894649".
- Structural_risk_minimization hasPhotoCollection Structural_risk_minimization.
- Structural_risk_minimization subject Category:Machine_learning.
- Structural_risk_minimization comment "Structural risk minimization (SRM) is an inductive principle of use in machine learning. Commonly in machine learning, a generalized model must be selected from a finite data set, with the consequent problem of overfitting – the model becoming too strongly tailored to the particularities of the training set and generalizing poorly to new data.".
- Structural_risk_minimization label "Structural risk minimization".
- Structural_risk_minimization sameAs m.02qmr65.
- Structural_risk_minimization sameAs Q7625053.
- Structural_risk_minimization sameAs Q7625053.
- Structural_risk_minimization wasDerivedFrom Structural_risk_minimization?oldid=507894649.
- Structural_risk_minimization isPrimaryTopicOf Structural_risk_minimization.