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- Proximal_gradient_methods_for_learning abstract "Proximal gradient (forward backward splitting) methods for learning is an area of research in optimization and statistical learning theory which studies algorithms for a general class of convex regularization problems where the regularization penalty may not be differentiable. One such example is regularization (also known as Lasso) of the formProximal gradient methods offer a general framework for solving regularization problems from statistical learning theory with penalties that are tailored to a specific problem application. Such customized penalties can help to induce certain structure in problem solutions, such as sparsity (in the case of lasso) or group structure (in the case of group lasso).".
- Proximal_gradient_methods_for_learning wikiPageID "41200806".
- Proximal_gradient_methods_for_learning wikiPageRevisionID "592233990".
- Proximal_gradient_methods_for_learning subject Category:Convex_optimization.
- Proximal_gradient_methods_for_learning subject Category:First_order_methods.
- Proximal_gradient_methods_for_learning subject Category:Machine_learning.
- Proximal_gradient_methods_for_learning comment "Proximal gradient (forward backward splitting) methods for learning is an area of research in optimization and statistical learning theory which studies algorithms for a general class of convex regularization problems where the regularization penalty may not be differentiable.".
- Proximal_gradient_methods_for_learning label "Proximal gradient methods for learning".
- Proximal_gradient_methods_for_learning sameAs m.0_frgyy.
- Proximal_gradient_methods_for_learning sameAs Q17086776.
- Proximal_gradient_methods_for_learning sameAs Q17086776.
- Proximal_gradient_methods_for_learning wasDerivedFrom Proximal_gradient_methods_for_learning?oldid=592233990.
- Proximal_gradient_methods_for_learning isPrimaryTopicOf Proximal_gradient_methods_for_learning.