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Regularized Support Vector Machine

Regularized Support Vector Machine extends the classic SVM by explicitly controlling the trade-off between margin maximization and training error through an L1 or L2 penalty parameter. The soft-margin formulation introduced by Cortes and Vapnik in 1995 is itself a regularized model, and later L1-SVM variants additionally promote feature sparsity, enabling automatic variable selection in high-dimensional settings.

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Regularized Support Vector Machine (L1/L2-penalized SVM)

Taksonomisk metoderegistrering · ml-model / machine-learning

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