Optimal learning rates of \(l^p\)-type multiple kernel learning under general conditions

DOI10.1016/j.ins.2014.09.011zbMath1360.68691OpenAlexW2089590069MaRDI QIDQ526680

Fanyin Zhou, Shao-Gao Lv

Publication date: 15 May 2017

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

Full work available at URL: https://doi.org/10.1016/j.ins.2014.09.011

zbMATH Keywords

generalization abilitycorrelation measuremultiple kernel learninglocal Rademacher complexitykernel learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (2)

An efficient kernel learning algorithm for semisupervised regression problems ⋮ A high-order norm-product regularized multiple kernel learning framework for kernel optimization

Uses Software

• ElemStatLearn

Cites Work

• A fast algorithm for manifold learning by posing it as a symmetric diagonally dominant linear system
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• Statistical performance of support vector machines
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• Learning rates of least-square regularized regression
• Local Rademacher complexities
• On the mathematical foundations of learning
• Support Vector Machines
• A Reproducing Kernel Hilbert Space Framework for Spike Train Signal Processing
• Advanced Lectures on Machine Learning
• Choosing multiple parameters for support vector machines
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