Accurate On-line Support Vector Regression

DOI10.1162/089976603322385117zbMath1085.68640OpenAlexW2098263691WikidataQ62493417 ScholiaQ62493417MaRDI QIDQ4813883

Junshui Ma, James Theiler, Simon Perkins

Publication date: 7 September 2004

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

Full work available at URL: https://doi.org/10.1162/089976603322385117

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (12)

Efficient leave-\(m\)-out cross-validation of support vector regression by generalizing decremental algorithm ⋮ An adaptive support vector regressor controller for nonlinear systems ⋮ Incremental learning for \(\nu\)-support vector regression ⋮ Nonparallel support vector regression model and its SMO-type solver ⋮ Multi-parametric solution-path algorithm for instance-weighted support vector machines ⋮ Online Learning of Single- and Multivalued Functions with an Infinite Mixture of Linear Experts ⋮ Real-time model learning using incremental sparse spectrum Gaussian process regression ⋮ Online-SVR-compensated nonlinear generalized predictive control for hypersonic vehicles ⋮ Traditional and recent approaches in background modeling for foreground detection: an overview ⋮ Efficient Computation and Model Selection for the Support Vector Regression ⋮ Online chaotic time series prediction using unbiased composite kernel machine via Cholesky factorization ⋮ Incremental learning of dynamic fuzzy neural networks for accurate system modeling

Cites Work

• Warm-Start Strategies in Interior-Point Methods for Linear Programming
• 10.1162/15324430260185600
• Training v-Support Vector Regression: Theory and Algorithms
• Reoptimization With the Primal-Dual Interior Point Method
• LOQO:an interior point code for quadratic programming
• Oscillation and Chaos in Physiological Control Systems

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