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Glosarium AI

Kamus lengkap Kecerdasan Buatan

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Elastic Net Regularization

Combination of L1 and L2 regularizations that uses a mixing parameter to simultaneously benefit from variable selection (L1) and coefficient stabilization (L2).

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Regularization Hyperparameter (λ)

Scalar parameter that controls the intensity of penalization in regularization methods, balancing data fitting and model complexity.

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Regularization Factor

Multiplicative coefficient applied to the regularization term in the objective function, determining the relative weight of the penalty compared to the approximation error.

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Regularization Bias

Systematic bias introduced by regularization in parameter estimates, necessary to reduce variance and improve generalization.

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Frobenius Penalty

Matrix regularization term based on the Frobenius norm, penalizing the sum of squares of all matrix elements to control its complexity.

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Nuclear Regularization

Penalization based on the nuclear norm (sum of singular values) that promotes low-rank matrices, particularly useful in matrix completion.

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Cross-Validation for λ

Systematic evaluation procedure using different data partitions to select the optimal value of the regularization hyperparameter λ.

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Factor Degeneracy

Problem where latent factors become arbitrarily large in amplitude without improving approximation quality, requiring regularization to constrain their norm.

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Trace regularization

Penalty term based on the trace (sum of diagonal elements) of a matrix, used to control the global scale of factors in decomposition.

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Sparsity coefficient

Parameter controlling the intensity of L1 regularization, determining the desired level of sparsity in the representation of latent factors.

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Tikhonov penalty

Generalized form of L2 regularization applied to inverse problems, stabilizing the solution by penalizing the norm of parameters according to a predefined weighting matrix.

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Adaptive regularization adjustment

Method where the regularization parameter varies dynamically based on the local structure of the data, applying differentiated penalties according to regions of the space.

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