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Mall, R. et al. RGBM: regularized gradient boosting machines for identification of the transcriptional regulators of discrete glioma subtypes.
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The quality of the solution was controlled by additional matrix blocks, which regularize velocity gradients between neighboring nodes.
Finally, a robust L1-regularized gradient boosting is proposed to integrate our part-sense sparse features learner into an object classifier.
'GPA' for the gradient-projection algorithm and 'RGPA' for the regularized gradient-projection algorithm. .
Then we use the regularized gradient-projection composite iterative method to find a unique solution of ((A+B)^{-1}0cap U).
However, in this paper, we use the regularized gradient-projection algorithm to find the unique solution of the problems of constrained convex minimization problem and the zero points of maximal monotone operator, which also solves a certain variational inequality.
In this article, based on Marino and Xu's method, an iterative method which combines the regularized gradient-projection algorithm (RGPA) and the averaged mappings approach is proposed for finding a common solution of equilibrium and constrained convex minimization problems.
In this article, we use the regularized gradient-projection algorithm to find the minimum-norm solution of the constrained convex minimization problem, where (0<lambda<frac {2}{L+2}).
However, in this paper, for solving constrained convex minimization problems and finding zeros of the sum of two operators in Hilbert spaces, we use two algorithms; one is the gradient-projection algorithm (GPA), the other is the regularized gradient-projection algorithm (RGPA).
In this paper, based on the viscosity approximation method and the regularized gradient-projection algorithm, we find a common element of the solution set of a constrained convex minimization problem and the set of zero points of the maximal monotone operator problem.
'⇀' for weak convergence and '→' for strong convergence; (operatorname{Fix}(T)) denotes the set of fixed points of the mapping T; U denotes the solution set of (1.2). 'GPA' for the gradient-projection algorithm and 'RGPA' for the regularized gradient-projection algorithm.
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