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• W65749922 abstract "In this work the authors implement in a Multi-Layer Perceptron (MLP) environment a new class of quasi-newtonian (QN) methods. The algorithms proposed in the present paper use in the iterative scheme of a generalized BFGS-method a family of matrix algebras, recently introduced for displacement decompositions and for optimal preconditioning. This novel approach allows to construct methods having an O(n log_2 n) complexity. Numerical experiences compared with the performances of the best QN-algorithms known in the literature confirm the effectiveness of these new optimization techniques." @default.
• W65749922 created "2016-06-24" @default.
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• W65749922 date "1999-01-01" @default.
• W65749922 modified "2023-09-27" @default.
• W65749922 title "Matrix algebras in quasi-newtonian algorithms for optimal learning in multi-layer perceptrons" @default.
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