Stochastic gradient descent with Barzilai-Borwein update step for SVM

DOI10.1016/j.ins.2015.03.073zbMath1390.68555OpenAlexW573246709MaRDI QIDQ1749833

Paweł Drozda, Krzysztof Sopyła

Publication date: 17 May 2018

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2015.03.073

zbMATH Keywords

classificationonline learningstochastic gradient descentSVMBarzilai-Borwein

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (7)

Accelerating mini-batch SARAH by step size rules ⋮ Adaptive step size rules for stochastic optimization in large-scale learning ⋮ Insensitive stochastic gradient twin support vector machines for large scale problems ⋮ Properties of the sign gradient descent algorithms ⋮ Parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function ⋮ Accelerated augmented Lagrangian method for total variation minimization ⋮ NEW ADAPTIVE BARZILAI–BORWEIN STEP SIZE AND ITS APPLICATION IN SOLVING LARGE-SCALE OPTIMIZATION PROBLEMS

Uses Software

• L-BFGS
• LIBLINEAR
• Pegasos
• SGD-QN

Cites Work

• Gradient methods with adaptive step-sizes
• Cutting-plane training of structural SVMs
• R-linear convergence of the Barzilai and Borwein gradient method
• Large-Scale Machine Learning with Stochastic Gradient Descent
• Two-Point Step Size Gradient Methods
• Updating Quasi-Newton Matrices with Limited Storage
• Acceleration of Stochastic Approximation by Averaging
• Gradient Method with Retards and Generalizations
• A Stochastic Approximation Method
• Adaptive two-point stepsize gradient algorithm
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