Subsampled Hessian Newton Methods for Supervised Learning

DOI10.1162/NECO_a_00751zbMath1472.68162OpenAlexW2103346443WikidataQ40830680 ScholiaQ40830680MaRDI QIDQ5380307

Chun-Heng Huang, Chih-Jen Lin, Chien-Chih Wang

Publication date: 4 June 2019

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/neco_a_00751

Mathematics Subject Classification ID

Convex programming (90C25) Large-scale problems in mathematical programming (90C06) Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Sub-sampled Newton methods, Distributed Newton Methods for Deep Neural Networks

Uses Software

• LIBLINEAR

Cites Work

• Performance of first-order methods for smooth convex minimization: a novel approach
• On the Use of Stochastic Hessian Information in Optimization Methods for Machine Learning
• Fast Curvature Matrix-Vector Products for Second-Order Gradient Descent
• A finite newton method for classification
• Trust Region Methods
• A simple automatic derivative evaluation program
• Some methods of speeding up the convergence of iteration methods
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