Feature-aware regularization for sparse online learning

DOI10.1007/s11432-014-5082-zzbMath1343.68202OpenAlexW1997378987MaRDI QIDQ893629

Shin Matsushima, Hidekazu Oiwa, Hiroshi Nakagawa

Publication date: 20 November 2015

Published in: Science China. Information Sciences (Search for Journal in Brave)

Full work available at URL: http://engine.scichina.com/doi/10.1007/s11432-014-5082-z

zbMATH Keywords

supervised learningonline learningfeature selectionsentiment analysissparsity-inducing regularization

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

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Uses Software

• OSCAR
• AdaGrad

Cites Work

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• Primal-dual subgradient methods for convex problems
• A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
• Approximation accuracy, gradient methods, and error bound for structured convex optimization
• A primal-dual perspective of online learning algorithms
• Mirror descent and nonlinear projected subgradient methods for convex optimization.
• Efficient algorithms for online decision problems
• Online Learning and Online Convex Optimization
• Regularization and Variable Selection Via the Elastic Net
• Optimal Distributed Online Prediction using Mini-Batches
• Simultaneous Regression Shrinkage, Variable Selection, and Supervised Clustering of Predictors with OSCAR

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